Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168185
C. Ge, I. Gu, Jie Yang
This paper addresses issues in human fall detection from videos. Unlike using handcrafted features in the conventional machine learning, we extract features from Convolutional Neural Networks (CNNs) for human fall detection. Similar to many existing work using two stream inputs, we use a spatial CNN stream with raw image difference and a temporal CNN stream with optical flow as the inputs of CNN. Different from conventional two stream action recognition work, we exploit sparse representation with residual-based pooling on the CNN extracted features, for obtaining more discriminative feature codes. For characterizing the sequential information in video activity, we use the code vector from long-range dynamic feature representation by concatenating codes in segment-levels as the input to a SVM classifier. Experiments have been conducted on two public video databases for fall detection. Comparisons with six existing methods show the effectiveness of the proposed method.
{"title":"Human fall detection using segment-level cnn features and sparse dictionary learning","authors":"C. Ge, I. Gu, Jie Yang","doi":"10.1109/MLSP.2017.8168185","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168185","url":null,"abstract":"This paper addresses issues in human fall detection from videos. Unlike using handcrafted features in the conventional machine learning, we extract features from Convolutional Neural Networks (CNNs) for human fall detection. Similar to many existing work using two stream inputs, we use a spatial CNN stream with raw image difference and a temporal CNN stream with optical flow as the inputs of CNN. Different from conventional two stream action recognition work, we exploit sparse representation with residual-based pooling on the CNN extracted features, for obtaining more discriminative feature codes. For characterizing the sequential information in video activity, we use the code vector from long-range dynamic feature representation by concatenating codes in segment-levels as the input to a SVM classifier. Experiments have been conducted on two public video databases for fall detection. Comparisons with six existing methods show the effectiveness of the proposed method.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"17 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77713093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168135
D. Fantinato, A. Neves, D. G. Silva, R. Attux
In communication systems, the study of elements and structures defined over Galois fields are generally limited to data coding. However, in this work, a novel perspective that combines data coding and channel equalization is considered to compose a simplified communication system over the field. Besides the coding advantages, this framework is able to restore distortions or malfunctioning processes, and can be potentially applied in network coding models. Interestingly, the operation of the equalizer is possible from a blind standpoint through the exploration of the redundant information introduced by the encoder. More specifically, we define a blind equalization criterion based on the matching of probability mass functions (PMFs) via the Kullback-Leibler divergence. Simulations involving the main aspects of the equalizer and the criterion are performed, including the use of a genetic algorithm to aid the search for the solution, with promising results.
{"title":"Blind channel equalization of encoded data over galois fields","authors":"D. Fantinato, A. Neves, D. G. Silva, R. Attux","doi":"10.1109/MLSP.2017.8168135","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168135","url":null,"abstract":"In communication systems, the study of elements and structures defined over Galois fields are generally limited to data coding. However, in this work, a novel perspective that combines data coding and channel equalization is considered to compose a simplified communication system over the field. Besides the coding advantages, this framework is able to restore distortions or malfunctioning processes, and can be potentially applied in network coding models. Interestingly, the operation of the equalizer is possible from a blind standpoint through the exploration of the redundant information introduced by the encoder. More specifically, we define a blind equalization criterion based on the matching of probability mass functions (PMFs) via the Kullback-Leibler divergence. Simulations involving the main aspects of the equalizer and the criterion are performed, including the use of a genetic algorithm to aid the search for the solution, with promising results.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"158 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86347607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168187
Karl Øyvind Mikalsen, F. Bianchi, C. Soguero-Ruíz, R. Jenssen
This paper presents the time series cluster kernel (TCK) for multivariate time series with missing data. Our approach leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with empirical prior distributions. Further, we exploit an ensemble learning approach to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel. In comparative experiments, we demonstrate that the TCK is robust to parameter choices and illustrate its capabilities of dealing with multivariate time series, both with and without missing data.
{"title":"The time series cluster kernel","authors":"Karl Øyvind Mikalsen, F. Bianchi, C. Soguero-Ruíz, R. Jenssen","doi":"10.1109/MLSP.2017.8168187","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168187","url":null,"abstract":"This paper presents the time series cluster kernel (TCK) for multivariate time series with missing data. Our approach leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with empirical prior distributions. Further, we exploit an ensemble learning approach to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel. In comparative experiments, we demonstrate that the TCK is robust to parameter choices and illustrate its capabilities of dealing with multivariate time series, both with and without missing data.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"58 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80008304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168186
Alan Joseph Bekker, M. Chorev, L. Carmel, J. Goldberger
An appreciable fraction of introns is thought to be involved in cellular functions, but there is no obvious way to predict which specific intron is likely to be functional. For each intron we are given a feature representation that is based on its evolutionary patterns. For a small subsets of introns we are also given an indication that they are functional. For all other introns it is not known whether they are functional or not. Our task is to estimate what fraction of introns are functional and, how likely it is that each individual intron is functional. We define a probabilistic classification model that treats the given functionality labels as noisy versions of labels created by a Deep Neural Network model. The maximum-likelihood model parameters are found by utilizing the Expectation-Maximization algorithm. We show that roughly 80% of the functional introns are still not recognized as such, and that roughly a third of all introns are functional.
{"title":"A deep neural network witharestricted noisy channel for identification of functional introns","authors":"Alan Joseph Bekker, M. Chorev, L. Carmel, J. Goldberger","doi":"10.1109/MLSP.2017.8168186","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168186","url":null,"abstract":"An appreciable fraction of introns is thought to be involved in cellular functions, but there is no obvious way to predict which specific intron is likely to be functional. For each intron we are given a feature representation that is based on its evolutionary patterns. For a small subsets of introns we are also given an indication that they are functional. For all other introns it is not known whether they are functional or not. Our task is to estimate what fraction of introns are functional and, how likely it is that each individual intron is functional. We define a probabilistic classification model that treats the given functionality labels as noisy versions of labels created by a Deep Neural Network model. The maximum-likelihood model parameters are found by utilizing the Expectation-Maximization algorithm. We show that roughly 80% of the functional introns are still not recognized as such, and that roughly a third of all introns are functional.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"40 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77600545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168176
K. Mentl, B. Mailhé, Florin C. Ghesu, Frank Schebesch, T. Haderlein, A. Maier, M. Nadar
This article presents a novel neural network-based approach for enhancement of 3D medical image data. The proposed networks learn a sparse representation basis by mapping the corrupted input data to corresponding optimal targets. To reinforce the adjustment of the network to the given data, the threshold values are also adaptively learned. In order to capture important image features on various scales and be able to process large computed tomography (CT) volumes in a reasonable time, a multiscale approach is applied. Recursively downsampled versions of the input are used and denoising operator of constant size are learnt at each scale. The networks are trained end-to-end from a database of real highdose acquisitions with synthetic additional noise to simulate the corresponding low-dose scans. Both 2D and 3D networks are evaluated on CT volumes and compared to the block-matching and 3D filtering (BM3D) algorithm. The presented methods achieve an increase of 4% to 11% in the SSIM and of 2.4 to 2.8 dB in the PSNR with respect to the ground truth, outperform BM3D in quantitative comparisions and present no visible texture artifacts. By exploiting volumetric information, 3D networks achieve superior results over 2D networks.
{"title":"Noise reduction in low-dose ct using a 3D multiscale sparse denoising autoencoder","authors":"K. Mentl, B. Mailhé, Florin C. Ghesu, Frank Schebesch, T. Haderlein, A. Maier, M. Nadar","doi":"10.1109/MLSP.2017.8168176","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168176","url":null,"abstract":"This article presents a novel neural network-based approach for enhancement of 3D medical image data. The proposed networks learn a sparse representation basis by mapping the corrupted input data to corresponding optimal targets. To reinforce the adjustment of the network to the given data, the threshold values are also adaptively learned. In order to capture important image features on various scales and be able to process large computed tomography (CT) volumes in a reasonable time, a multiscale approach is applied. Recursively downsampled versions of the input are used and denoising operator of constant size are learnt at each scale. The networks are trained end-to-end from a database of real highdose acquisitions with synthetic additional noise to simulate the corresponding low-dose scans. Both 2D and 3D networks are evaluated on CT volumes and compared to the block-matching and 3D filtering (BM3D) algorithm. The presented methods achieve an increase of 4% to 11% in the SSIM and of 2.4 to 2.8 dB in the PSNR with respect to the ground truth, outperform BM3D in quantitative comparisions and present no visible texture artifacts. By exploiting volumetric information, 3D networks achieve superior results over 2D networks.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"6 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87680203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168145
E. Nadimi, V. Blanes-Vidal
In this paper, we regarded an absorbing inhomogeneous medium as an assembly of thin layers having different propagation properties. We derived a stochastic model for the refractive index and formulated the localisation problem given noisy distance measurements using graph realisation problem. We relaxed the problem using semi-definite programming (SDP) approach in lp realisation domain and derived upper bounds that follow Edmundson-Madansky bound of order 6p (EM6p) on the SDP objective function to provide an estimation of the techniques' localisation accuracy. Our results showed that the inhomogeneity of the media and the choice of lp norm have significant impact on the ratio of the expected value of the localisation error to the upper bound for the expected optimal SDP objective value. The tightest ratio was derived when l∞ norm was used.
{"title":"Upper bound performance of semi-definite programming for localisation in inhomogeneous media","authors":"E. Nadimi, V. Blanes-Vidal","doi":"10.1109/MLSP.2017.8168145","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168145","url":null,"abstract":"In this paper, we regarded an absorbing inhomogeneous medium as an assembly of thin layers having different propagation properties. We derived a stochastic model for the refractive index and formulated the localisation problem given noisy distance measurements using graph realisation problem. We relaxed the problem using semi-definite programming (SDP) approach in lp realisation domain and derived upper bounds that follow Edmundson-Madansky bound of order 6p (EM6p) on the SDP objective function to provide an estimation of the techniques' localisation accuracy. Our results showed that the inhomogeneity of the media and the choice of lp norm have significant impact on the ratio of the expected value of the localisation error to the upper bound for the expected optimal SDP objective value. The tightest ratio was derived when l∞ norm was used.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90588609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168106
Tran Duy Linh, Masayuki Arai
In the present paper, we propose a deep network architecture in order to improve the accuracy of pedestrian detection. The proposed method contains a proposal network and a classification network that are trained separately. We use a single shot multibox detector (SSD) as a proposal network to generate the set of pedestrian proposals. The proposal network is fine-tuned from a pre-trained network by several pedestrian data sets of large input size (512 × 512 pixels) in order to improve detection accuracy of small pedestrians. Then, we use a classification network to classify pedestrian proposals. We then combine the scores from the proposal network and the classification network to obtain better final detection scores. Experiments were evaluated using the Caltech test set, and, compared to other state-of-the-art methods of pedestrian detection task, the proposed method obtains better results for small pedestrians (30 to 50 pixels in height) with an average miss rate of 42%.
{"title":"A two-stage training deep neural network for small pedestrian detection","authors":"Tran Duy Linh, Masayuki Arai","doi":"10.1109/MLSP.2017.8168106","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168106","url":null,"abstract":"In the present paper, we propose a deep network architecture in order to improve the accuracy of pedestrian detection. The proposed method contains a proposal network and a classification network that are trained separately. We use a single shot multibox detector (SSD) as a proposal network to generate the set of pedestrian proposals. The proposal network is fine-tuned from a pre-trained network by several pedestrian data sets of large input size (512 × 512 pixels) in order to improve detection accuracy of small pedestrians. Then, we use a classification network to classify pedestrian proposals. We then combine the scores from the proposal network and the classification network to obtain better final detection scores. Experiments were evaluated using the Caltech test set, and, compared to other state-of-the-art methods of pedestrian detection task, the proposed method obtains better results for small pedestrians (30 to 50 pixels in height) with an average miss rate of 42%.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"44 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85674323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168127
Haruki Mori, Tetsuya Youkawa, S. Izumi, M. Yoshimoto, H. Kawaguchi, Atsuki Inoue
This paper describes a pipelined stochastic gradient descent (SGD) algorithm and its hardware architecture with a memory distributed structure. In the proposed architecture, a pipeline stage takes charge of multiple layers: a “layer block.” The layer-block-wise pipeline has much less weight parameters for network training than conventional multithreading because weight memory is distributed to workers assigned to pipeline stages. The memory capacity of 2.25 GB for the four-stage proposed pipeline is about half of the 3.82 GB for multithreading when a batch size is 32 in VGG-F. Unlike multithreaded data parallelism, no parameter server for weight update or shared I/O data bus is necessary. Therefore, the memory bandwidth is drastically reduced. The proposed four-stage pipeline only needs memory bandwidths of 36.3 MB and 17.0 MB per batch, respectively, for forward propagation and backpropagation processes, whereas four-thread multithreading requires a bandwidth of 974 MB overall for send and receive processes to unify its weight parameters. At the parallelization degree of four, the proposed pipeline maintains training convergence by a factor of 1.12, compared with the conventional multithreaded architecture although the memory capacity and the memory bandwidth are decreased.
{"title":"A layer-block-wise pipeline for memory and bandwidth reduction in distributed deep learning","authors":"Haruki Mori, Tetsuya Youkawa, S. Izumi, M. Yoshimoto, H. Kawaguchi, Atsuki Inoue","doi":"10.1109/MLSP.2017.8168127","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168127","url":null,"abstract":"This paper describes a pipelined stochastic gradient descent (SGD) algorithm and its hardware architecture with a memory distributed structure. In the proposed architecture, a pipeline stage takes charge of multiple layers: a “layer block.” The layer-block-wise pipeline has much less weight parameters for network training than conventional multithreading because weight memory is distributed to workers assigned to pipeline stages. The memory capacity of 2.25 GB for the four-stage proposed pipeline is about half of the 3.82 GB for multithreading when a batch size is 32 in VGG-F. Unlike multithreaded data parallelism, no parameter server for weight update or shared I/O data bus is necessary. Therefore, the memory bandwidth is drastically reduced. The proposed four-stage pipeline only needs memory bandwidths of 36.3 MB and 17.0 MB per batch, respectively, for forward propagation and backpropagation processes, whereas four-thread multithreading requires a bandwidth of 974 MB overall for send and receive processes to unify its weight parameters. At the parallelization degree of four, the proposed pipeline maintains training convergence by a factor of 1.12, compared with the conventional multithreaded architecture although the memory capacity and the memory bandwidth are decreased.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74535184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168113
B. Gatto, J. Colonna, E. M. Santos, E. Nakamura
Bioacoustics signals classification is an important instrument used in environmental monitoring as it gives the means to efficiently acquire information from the areas, which most of the time are unfeasible to approach. To address these challenges, bioacoustics signals classification systems should meet some requirements, such as low computational resources capabilities. In this paper, we propose a novel bioacoustics signals classification method where no preprocessing techniques are involved and which is able to match sets of signals. The advantages of our proposed method include: a novel and compact representation for bioacoustics signals, which is independent of the signals length. In addition, no preprocessing is required, such as segmentation, noise reduction or syllable extraction. We show that our method is theoretically and practically attractive through experimental results employing a publicity available bioacoustics signal dataset.
{"title":"Mutual singular spectrum analysis for bioacoustics classification","authors":"B. Gatto, J. Colonna, E. M. Santos, E. Nakamura","doi":"10.1109/MLSP.2017.8168113","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168113","url":null,"abstract":"Bioacoustics signals classification is an important instrument used in environmental monitoring as it gives the means to efficiently acquire information from the areas, which most of the time are unfeasible to approach. To address these challenges, bioacoustics signals classification systems should meet some requirements, such as low computational resources capabilities. In this paper, we propose a novel bioacoustics signals classification method where no preprocessing techniques are involved and which is able to match sets of signals. The advantages of our proposed method include: a novel and compact representation for bioacoustics signals, which is independent of the signals length. In addition, no preprocessing is required, such as segmentation, noise reduction or syllable extraction. We show that our method is theoretically and practically attractive through experimental results employing a publicity available bioacoustics signal dataset.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"325 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76548095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168146
A. Seghouane, Asif Iqbal
Sequential dictionary learning algorithms have been successfully applied to a number of image processing problems. In a number of these problems however, the data used for dictionary learning are structured matrices with notions of smoothness in the column direction. This prior information which can be traduced as a smoothness constraint on the learned dictionary atoms has not been included in existing dictionary learning algorithms. In this paper, we remedy to this situation by proposing a regularized sequential dictionary learning algorithm. The proposed algorithm differs from the existing ones in their dictionary update stage. The proposed algorithm generates smooth dictionary atoms via the solution of a regularized rank-one matrix approximation problem where regularization is introduced via penalization in the dictionary update stage. Experimental results on synthetic and real data illustrating the performance of the proposed algorithm are provided.
{"title":"A regularized sequential dictionary learning algorithm for fmri data analysis","authors":"A. Seghouane, Asif Iqbal","doi":"10.1109/MLSP.2017.8168146","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168146","url":null,"abstract":"Sequential dictionary learning algorithms have been successfully applied to a number of image processing problems. In a number of these problems however, the data used for dictionary learning are structured matrices with notions of smoothness in the column direction. This prior information which can be traduced as a smoothness constraint on the learned dictionary atoms has not been included in existing dictionary learning algorithms. In this paper, we remedy to this situation by proposing a regularized sequential dictionary learning algorithm. The proposed algorithm differs from the existing ones in their dictionary update stage. The proposed algorithm generates smooth dictionary atoms via the solution of a regularized rank-one matrix approximation problem where regularization is introduced via penalization in the dictionary update stage. Experimental results on synthetic and real data illustrating the performance of the proposed algorithm are provided.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"51 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79798684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：