Pub Date : 2017-09-01DOI: 10.1109/MLSP.2017.8168169
A. Liutkus, Kazuyoshi Yoshii
This paper presents an accelerated version of positive semidefinite tensor factorization (PSDTF) for blind source separation. PSDTF works better than nonnegative matrix factorization (NMF) by dropping the arguable assumption that audio signals can be whitened in the frequency domain by using short-term Fourier transform (STFT). Indeed, this assumption only holds true in an ideal situation where each frame is infinitely long and the target signal is completely stationary in each frame. PSDTF thus deals with full covariance matrices over frequency bins instead of forcing them to be diagonal as in NMF. Although PSDTF significantly outperforms NMF in terms of separation performance, it suffers from a heavy computational cost due to the repeated inversion of big covariance matrices. To solve this problem, we propose an intermediate model based on diagonal plus low-rank covariance matrices and derive the expectation-maximization (EM) algorithm for efficiently updating the parameters of PSDTF. Experimental results showed that our method can dramatically reduce the complexity of PSDTF by several orders of magnitude without a significant decrease in separation performance.
{"title":"A diagonal plus low-rank covariance model for computationally efficient source separation","authors":"A. Liutkus, Kazuyoshi Yoshii","doi":"10.1109/MLSP.2017.8168169","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168169","url":null,"abstract":"This paper presents an accelerated version of positive semidefinite tensor factorization (PSDTF) for blind source separation. PSDTF works better than nonnegative matrix factorization (NMF) by dropping the arguable assumption that audio signals can be whitened in the frequency domain by using short-term Fourier transform (STFT). Indeed, this assumption only holds true in an ideal situation where each frame is infinitely long and the target signal is completely stationary in each frame. PSDTF thus deals with full covariance matrices over frequency bins instead of forcing them to be diagonal as in NMF. Although PSDTF significantly outperforms NMF in terms of separation performance, it suffers from a heavy computational cost due to the repeated inversion of big covariance matrices. To solve this problem, we propose an intermediate model based on diagonal plus low-rank covariance matrices and derive the expectation-maximization (EM) algorithm for efficiently updating the parameters of PSDTF. Experimental results showed that our method can dramatically reduce the complexity of PSDTF by several orders of magnitude without a significant decrease in separation performance.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"34 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":"88310167","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.2017.8168192
Shi-Xue Wen, Jun Du, Chin-Hui Lee
We first examine the generalization issue with the noise samples used in training nonlinear mapping functions between noisy and clean speech features for deep neural network (DNN) based speech enhancement. Then an empirical proof is established to explain why the DNN-based approach has a good noise generalization capability provided that a large collection of noise types are included in generating diverse noisy speech samples for training. It is shown that an arbitrary noise signal segment can be well represented by a linear combination of microstructure noise bases. Accordingly, we propose to generate these mixing noise signals by designing a set of compact and analytic noise bases without using any realistic noise types. The experiments demonstrate that this noise generation scheme can yield comparable performance to that using 50 real noise types. Furthermore, by supplementing the collected noise types with the synthesized noise bases, we observe remarkable performance improvements implying that not only a large collection of real-world noise signals can be alleviated, but also a good noise generalization capability can be achieved.
{"title":"On generating mixing noise signals with basis functions for simulating noisy speech and learning dnn-based speech enhancement models","authors":"Shi-Xue Wen, Jun Du, Chin-Hui Lee","doi":"10.1109/MLSP.2017.8168192","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168192","url":null,"abstract":"We first examine the generalization issue with the noise samples used in training nonlinear mapping functions between noisy and clean speech features for deep neural network (DNN) based speech enhancement. Then an empirical proof is established to explain why the DNN-based approach has a good noise generalization capability provided that a large collection of noise types are included in generating diverse noisy speech samples for training. It is shown that an arbitrary noise signal segment can be well represented by a linear combination of microstructure noise bases. Accordingly, we propose to generate these mixing noise signals by designing a set of compact and analytic noise bases without using any realistic noise types. The experiments demonstrate that this noise generation scheme can yield comparable performance to that using 50 real noise types. Furthermore, by supplementing the collected noise types with the synthesized noise bases, we observe remarkable performance improvements implying that not only a large collection of real-world noise signals can be alleviated, but also a good noise generalization capability can be achieved.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"110 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":"75628645","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.2017.8168149
Abdolreza Shirvani, B. Oommen
Data in all Signal Processing (SP) applications is being generated super-exponentially, and at an ever increasing rate. A meaningful way to pre-process it so as to achieve feasible computation is by Partitioning the data [5]. Indeed, the task of partitioning is one of the most difficult problems in computing, and it has extensive applications in solving real-life problems, especially when the amount of SP data (i.e., images, voices, speakers, libraries etc.) to be processed is prohibitively large. The problem is known to be NP-hard. The benchmark solution for this for the Equi-partitioning Problem (EPP) has involved the classic field of Learning Automata (LA), and the corresponding algorithm, the Object Migrating Automata (OMA) has been used in numerous application domains. While the OMA is a fixed structure machine, it does not incorporate the Pursuit concept that has, recently, significantly enhanced the field of LA. In this paper, we pioneer the incorporation of the Pursuit concept into the OMA. We do this by a non-intuitive paradigm, namely that of removing (or discarding) from the query stream, queries that could be counter-productive. This can be perceived as a filtering agent triggered by a pursuit-based module. The resulting machine, referred to as the Pursuit OMA (POMA), has been rigorously tested in all the standard benchmark environments. Indeed, in certain extreme environments it is almost ten times faster than the original OMA. The application of the POMA to all signal processing applications is extremely promising.
所有信号处理(SP)应用中的数据都在以超级指数级的速度增长。对数据进行预处理以实现可行的计算是一种有意义的方法[5]。事实上，分区任务是计算中最困难的问题之一，它在解决现实问题方面有广泛的应用，特别是当要处理的SP数据(即图像、声音、扬声器、库等)的数量非常大时。这个问题被称为NP-hard。针对等分割问题(EPP)的基准解决方案涉及到学习自动机(LA)的经典领域，而相应的算法——对象迁移自动机(OMA)已经在许多应用领域得到了应用。虽然OMA是一个固定结构的机器，但它并没有融入最近在LA领域得到显著提升的Pursuit概念。在本文中，我们率先将追求概念纳入OMA。我们通过一种非直观的范例来做到这一点，即从查询流中删除(或丢弃)可能适得其反的查询。这可以看作是由基于追踪的模块触发的过滤代理。生成的机器称为Pursuit OMA (POMA)，已经在所有标准基准测试环境中进行了严格的测试。事实上，在某些极端环境下，它的速度几乎是原始OMA的十倍。POMA在所有信号处理应用中的应用是非常有前途的。
{"title":"Partitioning in signal processing using the object migration automaton and the pursuit paradigm","authors":"Abdolreza Shirvani, B. Oommen","doi":"10.1109/MLSP.2017.8168149","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168149","url":null,"abstract":"Data in all Signal Processing (SP) applications is being generated super-exponentially, and at an ever increasing rate. A meaningful way to pre-process it so as to achieve feasible computation is by Partitioning the data [5]. Indeed, the task of partitioning is one of the most difficult problems in computing, and it has extensive applications in solving real-life problems, especially when the amount of SP data (i.e., images, voices, speakers, libraries etc.) to be processed is prohibitively large. The problem is known to be NP-hard. The benchmark solution for this for the Equi-partitioning Problem (EPP) has involved the classic field of Learning Automata (LA), and the corresponding algorithm, the Object Migrating Automata (OMA) has been used in numerous application domains. While the OMA is a fixed structure machine, it does not incorporate the Pursuit concept that has, recently, significantly enhanced the field of LA. In this paper, we pioneer the incorporation of the Pursuit concept into the OMA. We do this by a non-intuitive paradigm, namely that of removing (or discarding) from the query stream, queries that could be counter-productive. This can be perceived as a filtering agent triggered by a pursuit-based module. The resulting machine, referred to as the Pursuit OMA (POMA), has been rigorously tested in all the standard benchmark environments. Indeed, in certain extreme environments it is almost ten times faster than the original OMA. The application of the POMA to all signal processing applications is extremely promising.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"440 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":"73598761","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.2017.8168142
Li Li, H. Kameoka, S. Makino
The non-negative matrix factorization (NMF) approach has shown to work reasonably well for monaural speech enhancement tasks. This paper proposes addressing two shortcomings of the original NMF approach: (1) the objective functions for the basis training and separation (Wiener filtering) are inconsistent (the basis spectra are not trained so that the separated signal becomes optimal); (2) minimizing spectral divergence measures does not necessarily lead to an enhancement in the feature domain (e.g., cepstral domain) or in terms of perceived quality. To address the first shortcoming, we have previously proposed an algorithm for Discriminative NMF (DNMF), which optimizes the same objective for basis training and separation. To address the second shortcoming, we have previously introduced novel frameworks called the cepstral distance regularized NMF (CDRNMF) and mel-generalized cepstral distance regularized NMF (MGCRNMF), which aim to enhance speech both in the spectral domain and feature domain. This paper proposes combining the goals of DNMF and MGCRNMF by incorporating the MGC regularizer into the DNMF objective function and proposes an algorithm for parameter estimation. The experimental results revealed that the proposed method outperformed the baseline approaches.
{"title":"Mel-Generalized cepstral regularization for discriminative non-negative matrix factorization","authors":"Li Li, H. Kameoka, S. Makino","doi":"10.1109/MLSP.2017.8168142","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168142","url":null,"abstract":"The non-negative matrix factorization (NMF) approach has shown to work reasonably well for monaural speech enhancement tasks. This paper proposes addressing two shortcomings of the original NMF approach: (1) the objective functions for the basis training and separation (Wiener filtering) are inconsistent (the basis spectra are not trained so that the separated signal becomes optimal); (2) minimizing spectral divergence measures does not necessarily lead to an enhancement in the feature domain (e.g., cepstral domain) or in terms of perceived quality. To address the first shortcoming, we have previously proposed an algorithm for Discriminative NMF (DNMF), which optimizes the same objective for basis training and separation. To address the second shortcoming, we have previously introduced novel frameworks called the cepstral distance regularized NMF (CDRNMF) and mel-generalized cepstral distance regularized NMF (MGCRNMF), which aim to enhance speech both in the spectral domain and feature domain. This paper proposes combining the goals of DNMF and MGCRNMF by incorporating the MGC regularizer into the DNMF objective function and proposes an algorithm for parameter estimation. The experimental results revealed that the proposed method outperformed the baseline approaches.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"2014 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":"88132290","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.2017.8168137
M. D. L. Alvarez, H. Hastie, D. Lane
Timeseries sensor data processing is indispensable for system monitoring. Working with autonomous vehicles requires mechanisms that provide insightful information about the status of a mission. In a setting where time and resources are limited, trajectory classification plays a vital role in mission monitoring and failure detection. In this context, we use navigational data to interpret trajectory patterns and classify them. We implement Long Short-Term Memory (LSTM) based Recursive Neural Networks (RNN) that learn the most commonly used survey trajectory patterns from surveys executed by two types of Autonomous Underwater Vehicles (AUV). We compare the performance of our network against baseline machine learning methods.
{"title":"Navigation-Based learning for survey trajectory classification in autonomous underwater vehicles","authors":"M. D. L. Alvarez, H. Hastie, D. Lane","doi":"10.1109/MLSP.2017.8168137","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168137","url":null,"abstract":"Timeseries sensor data processing is indispensable for system monitoring. Working with autonomous vehicles requires mechanisms that provide insightful information about the status of a mission. In a setting where time and resources are limited, trajectory classification plays a vital role in mission monitoring and failure detection. In this context, we use navigational data to interpret trajectory patterns and classify them. We implement Long Short-Term Memory (LSTM) based Recursive Neural Networks (RNN) that learn the most commonly used survey trajectory patterns from surveys executed by two types of Autonomous Underwater Vehicles (AUV). We compare the performance of our network against baseline machine learning methods.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"43 4 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":"80433944","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.2017.8168154
Akihiko Kasagi, T. Tabaru, H. Tamura
Convolutional neural networks (CNNs), in which several convolutional layers extract feature patterns from an input image, are one of the most popular network architectures used for image classification. The convolutional computation, however, requires a high computational cost, resulting in an increased power consumption and processing time. In this paper, we propose a novel algorithm that substitutes a single layer for a pair formed by a convolutional layer and the following average-pooling layer. The key idea of the proposed scheme is to compute the output of the pair of original layers without the computation of convolution. To achieve this end, our algorithm generates summed area tables (SATs) of input images first and directly computes the output values from the SATs. We implemented our algorithm for forward propagation and backward propagation to evaluate the performance. Our experimental results showed that our algorithm achieved 17.1 times faster performance than the original algorithm for the same parameter used in ResNet-34.
{"title":"Fast algorithm using summed area tables with unified layer performing convolution and average pooling","authors":"Akihiko Kasagi, T. Tabaru, H. Tamura","doi":"10.1109/MLSP.2017.8168154","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168154","url":null,"abstract":"Convolutional neural networks (CNNs), in which several convolutional layers extract feature patterns from an input image, are one of the most popular network architectures used for image classification. The convolutional computation, however, requires a high computational cost, resulting in an increased power consumption and processing time. In this paper, we propose a novel algorithm that substitutes a single layer for a pair formed by a convolutional layer and the following average-pooling layer. The key idea of the proposed scheme is to compute the output of the pair of original layers without the computation of convolution. To achieve this end, our algorithm generates summed area tables (SATs) of input images first and directly computes the output values from the SATs. We implemented our algorithm for forward propagation and backward propagation to evaluate the performance. Our experimental results showed that our algorithm achieved 17.1 times faster performance than the original algorithm for the same parameter used in ResNet-34.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"15 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":"84937810","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.2017.8168147
Kan Li, Ying Ma, J. Príncipe
In this paper, we propose a novel approach to automatically identify plant species using dynamics of plant growth and development or spatiotemporal evolution model (STEM). The online kernel adaptive autoregressive-moving-average (KAARMA) algorithm, a discrete-time dynamical system in the kernel reproducing Hilbert space (RKHS), is used to learn plant-development syntactic patterns from feature-vector sequences automatically extracted from 2D plant images, generated by stochastic L-systems. Results show multiclass KAARMA STEM can automatically identify plant species based on growth patterns. Furthermore, finite state machines extracted from trained KAARMA STEM retains competitive performance and are robust to noise. Automatically constructing an L-system or formal grammar to replicate a spatiotemporal structure is an open problem. This is an important first step to not only identify plants but also to generate realistic plant models automatically from observations.
{"title":"Automatic plant identification using stem automata","authors":"Kan Li, Ying Ma, J. Príncipe","doi":"10.1109/MLSP.2017.8168147","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168147","url":null,"abstract":"In this paper, we propose a novel approach to automatically identify plant species using dynamics of plant growth and development or spatiotemporal evolution model (STEM). The online kernel adaptive autoregressive-moving-average (KAARMA) algorithm, a discrete-time dynamical system in the kernel reproducing Hilbert space (RKHS), is used to learn plant-development syntactic patterns from feature-vector sequences automatically extracted from 2D plant images, generated by stochastic L-systems. Results show multiclass KAARMA STEM can automatically identify plant species based on growth patterns. Furthermore, finite state machines extracted from trained KAARMA STEM retains competitive performance and are robust to noise. Automatically constructing an L-system or formal grammar to replicate a spatiotemporal structure is an open problem. This is an important first step to not only identify plants but also to generate realistic plant models automatically from observations.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"21 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":"78059955","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.2017.8168196
A. Ozerov, Srdan Kitic, P. Pérez
We consider example-guided audio source separation approaches, where the audio mixture to be separated is supplied with source examples that are assumed matching the sources in the mixture both in frequency and time. These approaches were successfully applied to the tasks such as source separation by humming, score-informed music source separation, and music source separation guided by covers. Most of proposed methods are based on nonnegative matrix factorization (NMF) and its variants, including methods using NMF models pre-trained from examples as an initialization of mixture NMF decomposition, methods using those models as hyperparameters of priors of mixture NMF decomposition, and methods using coupled NMF models. Moreover, those methods differ by the choice of the NMF divergence and the NMF prior. However, there is no systematic comparison of all these methods. In this work, we compare existing methods and some new variants on the score-informed and cover-guided source separation tasks.
{"title":"A comparative study of example-guided audio source separation approaches based on nonnegative matrix factorization","authors":"A. Ozerov, Srdan Kitic, P. Pérez","doi":"10.1109/MLSP.2017.8168196","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168196","url":null,"abstract":"We consider example-guided audio source separation approaches, where the audio mixture to be separated is supplied with source examples that are assumed matching the sources in the mixture both in frequency and time. These approaches were successfully applied to the tasks such as source separation by humming, score-informed music source separation, and music source separation guided by covers. Most of proposed methods are based on nonnegative matrix factorization (NMF) and its variants, including methods using NMF models pre-trained from examples as an initialization of mixture NMF decomposition, methods using those models as hyperparameters of priors of mixture NMF decomposition, and methods using coupled NMF models. Moreover, those methods differ by the choice of the NMF divergence and the NMF prior. However, there is no systematic comparison of all these methods. In this work, we compare existing methods and some new variants on the score-informed and cover-guided source separation tasks.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"18 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":"81634470","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.2017.8168132
J. Dong, Badong Chen, N. Lu, Haixian Wang, Nanning Zheng
Common spatial patterns (CSP) is a widely used method in the field of electroencephalogram (EEG) signal processing. The goal of CSP is to find spatial filters that maximize the ratio between the variances of two classes. The conventional CSP is however sensitive to outliers because it is based on the L2-norm. Inspired by the correntropy induced metric (CIM), we propose in this work a new algorithm, called CIM based CSP (CSP-CIM), to improve the robustness of CSP with respect to outliers. The CSP-CIM searches the optimal solution by a simple gradient based iterative algorithm. A toy example and a real EEG dataset are used to demonstrate the desirable performance of the new method.
{"title":"Correntropy induced metric based common spatial patterns","authors":"J. Dong, Badong Chen, N. Lu, Haixian Wang, Nanning Zheng","doi":"10.1109/MLSP.2017.8168132","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168132","url":null,"abstract":"Common spatial patterns (CSP) is a widely used method in the field of electroencephalogram (EEG) signal processing. The goal of CSP is to find spatial filters that maximize the ratio between the variances of two classes. The conventional CSP is however sensitive to outliers because it is based on the L2-norm. Inspired by the correntropy induced metric (CIM), we propose in this work a new algorithm, called CIM based CSP (CSP-CIM), to improve the robustness of CSP with respect to outliers. The CSP-CIM searches the optimal solution by a simple gradient based iterative algorithm. A toy example and a real EEG dataset are used to demonstrate the desirable performance of the new method.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"14 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":"81882011","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.2017.8168115
E. Nadimi, J. Herp, M. M. Buijs, V. Blanes-Vidal
We studied the problem of classifying textured-materials from their single-imaged appearance, under general viewing and illumination conditions, using the theory of random matrices. To evaluate the performance of our algorithm, two distinct databases of images were used: The CUReT database and our database of colorectal polyp images collected from patients undergoing colon capsule endoscopy for early cancer detection. During the learning stage, our classifier algorithm established the universality laws for the empirical spectral density of the largest singular value and normalized largest singular value of the image intensity matrix adapted to the eigenvalues of the information-plus-noise model. We showed that these two densities converge to the generalized extreme value (GEV-Frechet) and Gaussian G1 distribution with rate O(N1/2), respectively. To validate the algorithm, we introduced a set of unseen images to the algorithm. Misclassification rate of approximately 1%–6%, depending on the database, was obtained, which is superior to the reported values of 5%–45% in previous research studies.
{"title":"Texture classification from single uncalibrated images: Random matrix theory approach","authors":"E. Nadimi, J. Herp, M. M. Buijs, V. Blanes-Vidal","doi":"10.1109/MLSP.2017.8168115","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168115","url":null,"abstract":"We studied the problem of classifying textured-materials from their single-imaged appearance, under general viewing and illumination conditions, using the theory of random matrices. To evaluate the performance of our algorithm, two distinct databases of images were used: The CUReT database and our database of colorectal polyp images collected from patients undergoing colon capsule endoscopy for early cancer detection. During the learning stage, our classifier algorithm established the universality laws for the empirical spectral density of the largest singular value and normalized largest singular value of the image intensity matrix adapted to the eigenvalues of the information-plus-noise model. We showed that these two densities converge to the generalized extreme value (GEV-Frechet) and Gaussian G1 distribution with rate O(N1/2), respectively. To validate the algorithm, we introduced a set of unseen images to the algorithm. Misclassification rate of approximately 1%–6%, depending on the database, was obtained, which is superior to the reported values of 5%–45% in previous research studies.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"34 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":"87945035","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}
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