Pub Date : 2025-02-25DOI: 10.1109/OJSP.2025.3545337
Manjun Cui;Zhichao Zhang;Jie Han;Yunjie Chen;Chunzheng Cao
The convolution type of the Cohen's class time-frequency distribution (CCTFD) is a useful and effective time-frequency analysis tool for additive noises jamming signals. However, it can't meet the requirement of high-performance denoising under low signal-to-noise ratio conditions. In this paper, we define the generalized metaplectic convolution-based Cohen's class time-frequency distribution (GMC-CCTFD) by replacing the traditional convolution operator in CCTFD with the generalized convolution operator of metaplectic transform (MT). This new definition leverages the high degrees of freedom and flexibility of MT, improving performance in non-stationary signal analysis. We then establish a fundamental theory about the GMC-CCTFD's essential properties. By integrating the Wiener filter principle with the time-frequency filtering mechanism of GMC-CCTFD, we design a least-squares adaptive filter in the Wigner distribution-MT domain. This allows us to achieve adaptive filtering denoising based on GMC-CCTFD, giving birth to the least-squares adaptive filter-based GMC-CCTFD. Furthermore, we conduct several examples and apply the proposed filtering method to real-world datasets, demonstrating its superior performance in noise suppression compared to some state-of-the-art methods.
{"title":"Generalized Metaplectic Convolution-Based Cohen's Class Time-Frequency Distribution: Theory and Application","authors":"Manjun Cui;Zhichao Zhang;Jie Han;Yunjie Chen;Chunzheng Cao","doi":"10.1109/OJSP.2025.3545337","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3545337","url":null,"abstract":"The convolution type of the Cohen's class time-frequency distribution (CCTFD) is a useful and effective time-frequency analysis tool for additive noises jamming signals. However, it can't meet the requirement of high-performance denoising under low signal-to-noise ratio conditions. In this paper, we define the generalized metaplectic convolution-based Cohen's class time-frequency distribution (GMC-CCTFD) by replacing the traditional convolution operator in CCTFD with the generalized convolution operator of metaplectic transform (MT). This new definition leverages the high degrees of freedom and flexibility of MT, improving performance in non-stationary signal analysis. We then establish a fundamental theory about the GMC-CCTFD's essential properties. By integrating the Wiener filter principle with the time-frequency filtering mechanism of GMC-CCTFD, we design a least-squares adaptive filter in the Wigner distribution-MT domain. This allows us to achieve adaptive filtering denoising based on GMC-CCTFD, giving birth to the least-squares adaptive filter-based GMC-CCTFD. Furthermore, we conduct several examples and apply the proposed filtering method to real-world datasets, demonstrating its superior performance in noise suppression compared to some state-of-the-art methods.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"348-368"},"PeriodicalIF":2.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10902015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Image segmentation is an essential initial stage in several computer vision applications. However, unsupervised image segmentation is still a challenging task in some cases such as when objects with a similar visual appearance overlap. Unlike 2D images, 4D Light Fields (LFs) convey both spatial and angular scene information facilitating depth/disparity estimation, which can be further used to guide the segmentation. Existing 4D LF segmentation methods that target object level (i.e., mid-level and high-level) segmentation are typically semi-supervised or supervised with ground truth labels and mostly support only densely sampled 4D LFs. This paper proposes a novel unsupervised mid-level 4D LF Segmentation method using Graph Neural Networks (LFSGNN), which segments all LF views consistently. To achieve that, the 4D LF is represented as a hypergraph, whose hypernodes are obtained based on hyperpixel over-segmentation. Then, a graph neural network is used to extract deep features from the LF and assign segmentation labels to all hypernodes. Afterwards, the network parameters are updated iteratively to achieve better object separation using backpropagation. The proposed segmentation method supports both densely and sparsely sampled 4D LFs. Experimental results on synthetic and real 4D LF datasets show that the proposed method outperforms benchmark methods both in terms of segmentation spatial accuracy and angular consistency.
{"title":"Unsupervised Angularly Consistent 4D Light Field Segmentation Using Hyperpixels and a Graph Neural Network","authors":"Maryam Hamad;Caroline Conti;Paulo Nunes;Luís Ducla Soares","doi":"10.1109/OJSP.2025.3545356","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3545356","url":null,"abstract":"Image segmentation is an essential initial stage in several computer vision applications. However, unsupervised image segmentation is still a challenging task in some cases such as when objects with a similar visual appearance overlap. Unlike 2D images, 4D Light Fields (LFs) convey both spatial and angular scene information facilitating depth/disparity estimation, which can be further used to guide the segmentation. Existing 4D LF segmentation methods that target object level (i.e., mid-level and high-level) segmentation are typically semi-supervised or supervised with ground truth labels and mostly support only densely sampled 4D LFs. This paper proposes a novel unsupervised mid-level 4D LF Segmentation method using Graph Neural Networks (LFSGNN), which segments all LF views consistently. To achieve that, the 4D LF is represented as a hypergraph, whose hypernodes are obtained based on hyperpixel over-segmentation. Then, a graph neural network is used to extract deep features from the LF and assign segmentation labels to all hypernodes. Afterwards, the network parameters are updated iteratively to achieve better object separation using backpropagation. The proposed segmentation method supports both densely and sparsely sampled 4D LFs. Experimental results on synthetic and real 4D LF datasets show that the proposed method outperforms benchmark methods both in terms of segmentation spatial accuracy and angular consistency.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"333-347"},"PeriodicalIF":2.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10902629","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-24DOI: 10.1109/OJSP.2025.3545379
Saeed Ghoorchian;Steven Bilaj;Setareh Maghsudi
Sequential decision-making under uncertainty is often associated with long feedback delays. Such delays degrade the performance of the learning agent in identifying a subset of arms with the optimal collective reward in the long run. This problem becomes significantly challenging in a non-stationary environment with structural dependencies amongst the reward distributions associated with the arms. Therefore, besides adapting to delays and environmental changes, learning the causal relations alleviates the adverse effects of feedback delay on the decision-making process. We formalize the described setting as a non-stationary and delayed combinatorial semi-bandit problem with causally related rewards. We model the causal relations by a directed graph in a stationary structural equation model. The agent maximizes the long-term average payoff, defined as a linear function of the base arms' rewards. We develop a policy that learns the structural dependencies from delayed feedback and utilizes that to optimize the decision-making while adapting to drifts. We prove a regret bound for the performance of the proposed algorithm. Besides, we evaluate our method via numerical analysis using synthetic and real-world datasets to detect the regions that contribute the most to the spread of Covid-19 in Italy.
{"title":"Non-Stationary Delayed Combinatorial Semi-Bandit With Causally Related Rewards","authors":"Saeed Ghoorchian;Steven Bilaj;Setareh Maghsudi","doi":"10.1109/OJSP.2025.3545379","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3545379","url":null,"abstract":"Sequential decision-making under uncertainty is often associated with long feedback delays. Such delays degrade the performance of the learning agent in identifying a subset of arms with the optimal collective reward in the long run. This problem becomes significantly challenging in a non-stationary environment with structural dependencies amongst the reward distributions associated with the arms. Therefore, besides adapting to delays and environmental changes, learning the causal relations alleviates the adverse effects of feedback delay on the decision-making process. We formalize the described setting as a non-stationary and delayed combinatorial semi-bandit problem with causally related rewards. We model the causal relations by a directed graph in a stationary structural equation model. The agent maximizes the long-term average payoff, defined as a linear function of the base arms' rewards. We develop a policy that learns the structural dependencies from delayed feedback and utilizes that to optimize the decision-making while adapting to drifts. We prove a regret bound for the performance of the proposed algorithm. Besides, we evaluate our method via numerical analysis using synthetic and real-world datasets to detect the regions that contribute the most to the spread of Covid-19 in Italy.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"369-384"},"PeriodicalIF":2.9,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10902019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-24DOI: 10.1109/OJSP.2025.3545304
Kazu Mishiba
This paper proposes the SVD-based Guided Filter, designed to address key limitations of the original guided filter and its improved methods, providing better use of multi-channel guide images. First, we analyzed the guided filter framework, reinterpreting it from a patch-based perspective using singular value decomposition (SVD). This revealed that the original guided filter suppresses oscillatory components based on their eigenvalues. Building on this insight, we proposed a new filtering method that selectively suppresses or enhances these components through functions that respond to their eigenvalues. The proposed SVD-based Guided Filter offers improved control over edge preservation and noise reduction compared to the original guided filter and its improved methods, which often struggle to balance these tasks. We validated the proposed method across various image processing applications, including denoising, edge-preserving smoothing, detail enhancement, and edge-enhancing smoothing. The results demonstrated that the SVD-based Guided Filter consistently outperforms the original guided filter and its improved methods by making more effective use of color guide images. While the computational cost is slightly higher than the original guided filter, the method remains efficient and highly effective. Overall, the proposed SVD-based Guided Filter delivers notable improvements, offering a solid foundation for further advancements in guided filtering techniques.
{"title":"Extending Guided Filters Through Effective Utilization of Multi-Channel Guide Images Based on Singular Value Decomposition","authors":"Kazu Mishiba","doi":"10.1109/OJSP.2025.3545304","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3545304","url":null,"abstract":"This paper proposes the SVD-based Guided Filter, designed to address key limitations of the original guided filter and its improved methods, providing better use of multi-channel guide images. First, we analyzed the guided filter framework, reinterpreting it from a patch-based perspective using singular value decomposition (SVD). This revealed that the original guided filter suppresses oscillatory components based on their eigenvalues. Building on this insight, we proposed a new filtering method that selectively suppresses or enhances these components through functions that respond to their eigenvalues. The proposed SVD-based Guided Filter offers improved control over edge preservation and noise reduction compared to the original guided filter and its improved methods, which often struggle to balance these tasks. We validated the proposed method across various image processing applications, including denoising, edge-preserving smoothing, detail enhancement, and edge-enhancing smoothing. The results demonstrated that the SVD-based Guided Filter consistently outperforms the original guided filter and its improved methods by making more effective use of color guide images. While the computational cost is slightly higher than the original guided filter, the method remains efficient and highly effective. Overall, the proposed SVD-based Guided Filter delivers notable improvements, offering a solid foundation for further advancements in guided filtering techniques.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"385-397"},"PeriodicalIF":2.9,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10902178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-24DOI: 10.1109/OJSP.2025.3544891
Ana C. Caznok Silveira;Diedre S. do Carmo;Lucas H. Ueda;Denis G. Fantinato;Paula D. P. Costa;Leticia Rittner
Hyperspectralchannel reconstruction transforms a subsampled multispectral image into hyperspectral imaging, providing higher spectral resolution without a dedicated acquisition hardware and camera. Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (MST++) is a state-of-the-art channel reconstruction technique, but it faces memory limitations for high spatial-resolution images. In this context, we introduced VITMST++, a novel architecture incorporating Vision Transformer embeddings for spatial compression, multi-resolution image context, and a custom channel-weighted loss. Developed for the ICASSP 2024 HyperSkin Challenge, VITMST++ outperforms the state-of-the-art MST++ in both performance and computational efficiency in channel reconstruction. In this work, we perform a deeper analysis on the main aspects of VITMST++ efficiency, quantitative performance, and generalization to other datasets. Results show that VITMST++ achieves similar values of SAM and SSIM hyperspectral reconstruction metrics when compared to state-of-the-art methods, while consuming up to three fold less memory and needing up to 10 times fewer multiply-add operations.
{"title":"VITMST++: Efficient Hyperspectral Reconstruction Through Vision Transformer-Based Spatial Compression","authors":"Ana C. Caznok Silveira;Diedre S. do Carmo;Lucas H. Ueda;Denis G. Fantinato;Paula D. P. Costa;Leticia Rittner","doi":"10.1109/OJSP.2025.3544891","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3544891","url":null,"abstract":"Hyperspectralchannel reconstruction transforms a subsampled multispectral image into hyperspectral imaging, providing higher spectral resolution without a dedicated acquisition hardware and camera. Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (MST++) is a state-of-the-art channel reconstruction technique, but it faces memory limitations for high spatial-resolution images. In this context, we introduced VITMST++, a novel architecture incorporating Vision Transformer embeddings for spatial compression, multi-resolution image context, and a custom channel-weighted loss. Developed for the ICASSP 2024 HyperSkin Challenge, VITMST++ outperforms the state-of-the-art MST++ in both performance and computational efficiency in channel reconstruction. In this work, we perform a deeper analysis on the main aspects of VITMST++ efficiency, quantitative performance, and generalization to other datasets. Results show that VITMST++ achieves similar values of SAM and SSIM hyperspectral reconstruction metrics when compared to state-of-the-art methods, while consuming up to three fold less memory and needing up to 10 times fewer multiply-add operations.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"398-404"},"PeriodicalIF":2.9,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900394","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-30DOI: 10.1109/OJSP.2025.3536850
David Uliel;Raja Giryes
In unsupervised domain adaptation (UDA) labeled data is available for one domain (Source Domain) which is generated according to some distribution, and unlabeled data is available for a second domain (Target Domain) which is generated from a possibly different distribution but has the same task. The goal is to learn a model that performs well on the target domain although labels are available only for the source data. Many recent works attempt to align the source and the target domains by matching their marginal distributions in a learned feature space. In this paper, we address the domain difference as a nuisance, and enables better adaptability of the domains, by encouraging minimality of the target domain representation, disentanglement of the features, and a smoother feature space that cluster better the target data. To this end, we use the information bottleneck theory and a classical technique from the blind source separation framework, namely, ICA (independent components analysis). We show that these concepts can improve performance of leading domain adaptation methods on various domain adaptation benchmarks.
{"title":"Task Nuisance Filtration for Unsupervised Domain Adaptation","authors":"David Uliel;Raja Giryes","doi":"10.1109/OJSP.2025.3536850","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3536850","url":null,"abstract":"In unsupervised domain adaptation (UDA) labeled data is available for one domain (Source Domain) which is generated according to some distribution, and unlabeled data is available for a second domain (Target Domain) which is generated from a possibly different distribution but has the same task. The goal is to learn a model that performs well on the target domain although labels are available only for the source data. Many recent works attempt to align the source and the target domains by matching their marginal distributions in a learned feature space. In this paper, we address the domain difference as a nuisance, and enables better adaptability of the domains, by encouraging minimality of the target domain representation, disentanglement of the features, and a smoother feature space that cluster better the target data. To this end, we use the information bottleneck theory and a classical technique from the blind source separation framework, namely, ICA (independent components analysis). We show that these concepts can improve performance of leading domain adaptation methods on various domain adaptation benchmarks.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"303-311"},"PeriodicalIF":2.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858365","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-30DOI: 10.1109/OJSP.2025.3536853
Masahiro Kada;Ryota Yoshihashi;Satoshi Ikehata;Rei Kawakami;Ikuro Sato
Mixture of experts with a sparse expert selection rule has been gaining much attention recently because of its scalability without compromising inference time. However, unlike standard neural networks, sparse mixture-of-experts models inherently exhibit discontinuities in the output space, which may impede the acquisition of appropriate invariance to the input perturbations, leading to a deterioration of model performance for tasks such as classification. To address this issue, we propose Pairwise Router Consistency (PRC) that effectively penalizes the discontinuities occurring under natural deformations of input images. With the supervised loss, the use of PRC loss empirically improves classification accuracy on ImageNet-1 K, CIFAR-10, and CIFAR-100 datasets, compared to a baseline method. Notably, our method with 1-expert selection slightly outperforms the baseline method using 2-expert selection. We also confirmed that models trained with our method experience discontinuous changes less frequently under input perturbations.
{"title":"Robustifying Routers Against Input Perturbations for Sparse Mixture-of-Experts Vision Transformers","authors":"Masahiro Kada;Ryota Yoshihashi;Satoshi Ikehata;Rei Kawakami;Ikuro Sato","doi":"10.1109/OJSP.2025.3536853","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3536853","url":null,"abstract":"Mixture of experts with a sparse expert selection rule has been gaining much attention recently because of its scalability without compromising inference time. However, unlike standard neural networks, sparse mixture-of-experts models inherently exhibit discontinuities in the output space, which may impede the acquisition of appropriate invariance to the input perturbations, leading to a deterioration of model performance for tasks such as classification. To address this issue, we propose Pairwise Router Consistency (PRC) that effectively penalizes the discontinuities occurring under natural deformations of input images. With the supervised loss, the use of PRC loss empirically improves classification accuracy on ImageNet-1 K, CIFAR-10, and CIFAR-100 datasets, compared to a baseline method. Notably, our method with 1-expert selection slightly outperforms the baseline method using 2-expert selection. We also confirmed that models trained with our method experience discontinuous changes less frequently under input perturbations.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"276-283"},"PeriodicalIF":2.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-28DOI: 10.1109/OJSP.2025.3534686
Junghyun Koo;Gordon Wichern;François G. Germain;Sameer Khurana;Jonathan Le Roux
We introduce Self-Monitored Inference-Time INtervention (SMITIN), an approach for controlling an autoregressive generative music transformer using classifier probes. These simple logistic regression probes are trained on the output of each attention head in the transformer using a small dataset of audio examples both exhibiting and missing a specific musical trait (e.g., the presence/absence of drums, or real/synthetic music). We then steer the attention heads in the probe direction, ensuring the generative model output captures the desired musical trait. Additionally, we monitor the probe output to avoid adding an excessive amount of intervention into the autoregressive generation, which could lead to temporally incoherent music. We validate our results objectively and subjectively for both audio continuation and text-to-music applications, demonstrating the ability to add controls to large generative models for which retraining or even fine-tuning is impractical for most musicians. Audio samples of the proposed intervention approach are available on our demo page.
{"title":"SMITIN: Self-Monitored Inference-Time INtervention for Generative Music Transformers","authors":"Junghyun Koo;Gordon Wichern;François G. Germain;Sameer Khurana;Jonathan Le Roux","doi":"10.1109/OJSP.2025.3534686","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3534686","url":null,"abstract":"We introduce Self-Monitored Inference-Time INtervention (SMITIN), an approach for controlling an autoregressive generative music transformer using classifier probes. These simple logistic regression probes are trained on the output of each attention head in the transformer using a small dataset of audio examples both exhibiting and missing a specific musical trait (e.g., the presence/absence of drums, or real/synthetic music). We then steer the attention heads in the probe direction, ensuring the generative model output captures the desired musical trait. Additionally, we monitor the probe output to avoid adding an excessive amount of intervention into the autoregressive generation, which could lead to temporally incoherent music. We validate our results objectively and subjectively for both audio continuation and text-to-music applications, demonstrating the ability to add controls to large generative models for which retraining or even fine-tuning is impractical for most musicians. Audio samples of the proposed intervention approach are available on our <underline>demo page</u>.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"266-275"},"PeriodicalIF":2.9,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10856829","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1109/OJSP.2025.3534690
Yaman Kındap;Simon Godsill
Probabilistic dynamical models used in applications in tracking and prediction are typically assumed to be Gaussian noise driven motions since well-known inference algorithms can be applied to these models. However, in many real world examples deviations from Gaussianity are expected to appear, e.g., rapid changes in speed or direction, which cannot be reflected using processes with a smooth mean response. In this work, we introduce the non-Gaussian process (NGP) dynamical model which allow for straightforward modelling of heavy-tailed, non-Gaussian behaviours while retaining a tractable conditional Gaussian process (GP) structure through an infinite mixture of non-homogeneous GPs representation. We present two novel inference methodologies for these new models based on the conditionally Gaussian formulation of NGPs which are suitable for both MCMC and marginalised particle filtering algorithms. The results are demonstrated on synthetically generated data sets.
{"title":"Non-Gaussian Process Dynamical Models","authors":"Yaman Kındap;Simon Godsill","doi":"10.1109/OJSP.2025.3534690","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3534690","url":null,"abstract":"Probabilistic dynamical models used in applications in tracking and prediction are typically assumed to be Gaussian noise driven motions since well-known inference algorithms can be applied to these models. However, in many real world examples deviations from Gaussianity are expected to appear, e.g., rapid changes in speed or direction, which cannot be reflected using processes with a smooth mean response. In this work, we introduce the non-Gaussian process (NGP) dynamical model which allow for straightforward modelling of heavy-tailed, non-Gaussian behaviours while retaining a tractable conditional Gaussian process (GP) structure through an infinite mixture of non-homogeneous GPs representation. We present two novel inference methodologies for these new models based on the conditionally Gaussian formulation of NGPs which are suitable for both MCMC and marginalised particle filtering algorithms. The results are demonstrated on synthetically generated data sets.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"213-221"},"PeriodicalIF":2.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10854574","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1109/OJSP.2025.3534692
Samuel Rey;Bishwadeep Das;Elvin Isufi
This paper addresses the problem of online network topology inference for expanding graphs from a stream of spatiotemporal signals. Online algorithms for dynamic graph learning are crucial in delay-sensitive applications or when changes in topology occur rapidly. While existing works focus on inferring the connectivity within a fixed set of nodes, in practice, the graph can grow as new nodes join the network. This poses additional challenges like modeling temporal dynamics involving signals and graphs of different sizes. This growth also increases the computational complexity of the learning process, which may become prohibitive. To the best of our knowledge, this is the first work to tackle this setting. We propose a general online algorithm based on projected proximal gradient descent that accounts for the increasing graph size at each iteration. Recursively updating the sample covariance matrix is a key aspect of our approach. We introduce a strategy that enables different types of updates for nodes that just joined the network and for previously existing nodes. To provide further insights into the proposed method, we specialize it in Gaussian Markov random field settings, where we analyze the computational complexity and characterize the dynamic cumulative regret. Finally, we demonstrate the effectiveness of the proposed approach using both controlled experiments and real-world datasets from epidemic and financial networks.
{"title":"Online Learning of Expanding Graphs","authors":"Samuel Rey;Bishwadeep Das;Elvin Isufi","doi":"10.1109/OJSP.2025.3534692","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3534692","url":null,"abstract":"This paper addresses the problem of online network topology inference for expanding graphs from a stream of spatiotemporal signals. Online algorithms for dynamic graph learning are crucial in delay-sensitive applications or when changes in topology occur rapidly. While existing works focus on inferring the connectivity within a fixed set of nodes, in practice, the graph can grow as new nodes join the network. This poses additional challenges like modeling temporal dynamics involving signals and graphs of different sizes. This growth also increases the computational complexity of the learning process, which may become prohibitive. To the best of our knowledge, this is the first work to tackle this setting. We propose a general online algorithm based on projected proximal gradient descent that accounts for the increasing graph size at each iteration. Recursively updating the sample covariance matrix is a key aspect of our approach. We introduce a strategy that enables different types of updates for nodes that just joined the network and for previously existing nodes. To provide further insights into the proposed method, we specialize it in Gaussian Markov random field settings, where we analyze the computational complexity and characterize the dynamic cumulative regret. Finally, we demonstrate the effectiveness of the proposed approach using both controlled experiments and real-world datasets from epidemic and financial networks.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"247-255"},"PeriodicalIF":2.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10854617","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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