Pub Date : 2017-12-31DOI: 10.1109/CAMSAP.2017.8313156
Minh Trinh-Hoang, M. Viberg, M. Pesavento
In this paper, the partial relaxation approach is introduced and applied to DOA estimation using spectral search. Unlike existing methods like Capon or MUSIC which can be considered as single source approximations of multi-source estimation criteria, the proposed approach accounts for the existence of multiple sources. At each direction, the manifold structure of interfering signals impinging on the sensor array is relaxed, which results in closed form estimates for the interference parameters. The conventional multidimensional optimization problem reduces, thanks to this relaxation, to a simple spectral search. Following this principle, proposed estimators based on the Deterministic Maximum Likelihood, Weighted Subspace Fitting and Covariance Fitting method are derived. Simulation results show that the performance of the proposed estimators is superior to conventional methods especially in the case of low SNR and low number of snapshots, irrespectively of the special structure of the sensor array.
{"title":"Improved DOA estimators using partial relaxation approach","authors":"Minh Trinh-Hoang, M. Viberg, M. Pesavento","doi":"10.1109/CAMSAP.2017.8313156","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313156","url":null,"abstract":"In this paper, the partial relaxation approach is introduced and applied to DOA estimation using spectral search. Unlike existing methods like Capon or MUSIC which can be considered as single source approximations of multi-source estimation criteria, the proposed approach accounts for the existence of multiple sources. At each direction, the manifold structure of interfering signals impinging on the sensor array is relaxed, which results in closed form estimates for the interference parameters. The conventional multidimensional optimization problem reduces, thanks to this relaxation, to a simple spectral search. Following this principle, proposed estimators based on the Deterministic Maximum Likelihood, Weighted Subspace Fitting and Covariance Fitting method are derived. Simulation results show that the performance of the proposed estimators is superior to conventional methods especially in the case of low SNR and low number of snapshots, irrespectively of the special structure of the sensor array.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125333289","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 : 2017-12-29DOI: 10.1109/CAMSAP.2017.8313062
Yang Yang, M. Pesavento
In this paper, we consider the energy efficiency maximization problem in MIMO interference channels where all users have a guaranteed minimum transmission rate. To solve this optimization problem with a nonconcave objective function and a nonconvex constraint set, we extend the recently developed successive pseudoconvex approximation framework and propose a novel iterative algorithm that has the following advantages: 1) fast convergence, as the structure of the original optimization problem is preserved as much as possible in the approximate problem solved in each iteration, 2) efficient implementation, as each approximate problem is natural for parallel computation and its solution has a closed-form expression, and 3) guaranteed convergence to a Karush-Kuhn-Tucker (KKT) point.
{"title":"Energy efficient transmission in MIMO interference channels with QoS constraints","authors":"Yang Yang, M. Pesavento","doi":"10.1109/CAMSAP.2017.8313062","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313062","url":null,"abstract":"In this paper, we consider the energy efficiency maximization problem in MIMO interference channels where all users have a guaranteed minimum transmission rate. To solve this optimization problem with a nonconcave objective function and a nonconvex constraint set, we extend the recently developed successive pseudoconvex approximation framework and propose a novel iterative algorithm that has the following advantages: 1) fast convergence, as the structure of the original optimization problem is preserved as much as possible in the approximate problem solved in each iteration, 2) efficient implementation, as each approximate problem is natural for parallel computation and its solution has a closed-form expression, and 3) guaranteed convergence to a Karush-Kuhn-Tucker (KKT) point.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133591930","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 : 2017-12-10DOI: 10.1109/CAMSAP.2017.8313181
R. Martín-Clemente, V. Zarzoso
Principal component analysis (PCA) is an ubiquitous data compression and feature extraction technique in signal processing and machine learning. As compared with the classical L2-norm PCA, its L1-norm version offers increased robustness to outliers that are usually present in faulty data. Recently, L1-PCA was shown to perform source recovery when the observed data follow an independent component analysis (ICA) model. However, proof of this result requires the data to be sphered, i.e., to be preprocessed to constrain their covariance matrix to be the identity. The present contribution extends this result by relaxing the sphering assumption and allowing the data to have arbitrary covariance matrix. We prove that L1-PCA is indeed able to identify the mixing matrix columns associated with the strongest independent sources, thus performing signal subspace identification with improved robustness to outliers. Numerical experiments illustrate and confirm the theoretical findings.
{"title":"L1-PCA signal subspace identification for non-sphered data under the ICA model","authors":"R. Martín-Clemente, V. Zarzoso","doi":"10.1109/CAMSAP.2017.8313181","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313181","url":null,"abstract":"Principal component analysis (PCA) is an ubiquitous data compression and feature extraction technique in signal processing and machine learning. As compared with the classical L2-norm PCA, its L1-norm version offers increased robustness to outliers that are usually present in faulty data. Recently, L1-PCA was shown to perform source recovery when the observed data follow an independent component analysis (ICA) model. However, proof of this result requires the data to be sphered, i.e., to be preprocessed to constrain their covariance matrix to be the identity. The present contribution extends this result by relaxing the sphering assumption and allowing the data to have arbitrary covariance matrix. We prove that L1-PCA is indeed able to identify the mixing matrix columns associated with the strongest independent sources, thus performing signal subspace identification with improved robustness to outliers. Numerical experiments illustrate and confirm the theoretical findings.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125491318","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 : 2017-12-10DOI: 10.1109/CAMSAP.2017.8313081
Bruno Mériaux, Chengfang Ren, M. Korso, A. Breloy, P. Forster
This paper deals with structured covariance matrix estimation in a robust statistical framework. Covariance matrices often exhibit a particular structure related to the application of interest and taking this structure into account increases estimation accuracy. Within the framework of robust estimation, the class of circular Complex Elliptically Symmetric (CES) distributions is particularly interesting to handle impulsive and spiky data. Normalized CES random vectors are known to share a common Complex Angular Elliptical distribution. In this context, we propose a Robust Covariance Matrix Estimation Technique (RCOMET) based on Tyler's estimate and COMET criterion for convexly structured matrices. We prove that the proposed estimator is consistent and asymptotically efficient while computationally attractive. Numerical results support the theoretical analysis in a particular application for Hermitian Toeplitz structure.
{"title":"Robust-COMET for covariance estimation in convex structures: Algorithm and statistical properties","authors":"Bruno Mériaux, Chengfang Ren, M. Korso, A. Breloy, P. Forster","doi":"10.1109/CAMSAP.2017.8313081","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313081","url":null,"abstract":"This paper deals with structured covariance matrix estimation in a robust statistical framework. Covariance matrices often exhibit a particular structure related to the application of interest and taking this structure into account increases estimation accuracy. Within the framework of robust estimation, the class of circular Complex Elliptically Symmetric (CES) distributions is particularly interesting to handle impulsive and spiky data. Normalized CES random vectors are known to share a common Complex Angular Elliptical distribution. In this context, we propose a Robust Covariance Matrix Estimation Technique (RCOMET) based on Tyler's estimate and COMET criterion for convexly structured matrices. We prove that the proposed estimator is consistent and asymptotically efficient while computationally attractive. Numerical results support the theoretical analysis in a particular application for Hermitian Toeplitz structure.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134321412","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 : 2017-12-10DOI: 10.1109/CAMSAP.2017.8313113
Fraser K. Coutts, K. Thompson, I. Proudler, Stephan Weiss
A variety of algorithms have been developed to compute an approximate polynomial matrix eigenvalue decomposition (PEVD). As an extension of the ordinary EVD to polynomial matrices, the PEVD will generate paraunitary matrices that diagonalise a parahermitian matrix. This paper compares the decomposition accuracies of two fundamentally different methods capable of computing an approximate PEVD. The first of these — sequential matrix diagonalisation (SMD) — iteratively decomposes a parahermitian matrix, while the second DFT-based algorithm computes a pointwise in frequency decomposition. We demonstrate through the use of examples that both algorithms can achieve varying levels of decomposition accuracy, and provide results that indicate the type of broadband multichannel problems that are better suited to each algorithm. It is shown that iterative methods, which generate paraunitary eigenvectors, are suited for general applications with a low number of sensors, while a DFT-based approach is useful for fixed, finite order decompositions with a small number of lags.
{"title":"A comparison of iterative and DFT-Based polynomial matrix eigenvalue decompositions","authors":"Fraser K. Coutts, K. Thompson, I. Proudler, Stephan Weiss","doi":"10.1109/CAMSAP.2017.8313113","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313113","url":null,"abstract":"A variety of algorithms have been developed to compute an approximate polynomial matrix eigenvalue decomposition (PEVD). As an extension of the ordinary EVD to polynomial matrices, the PEVD will generate paraunitary matrices that diagonalise a parahermitian matrix. This paper compares the decomposition accuracies of two fundamentally different methods capable of computing an approximate PEVD. The first of these — sequential matrix diagonalisation (SMD) — iteratively decomposes a parahermitian matrix, while the second DFT-based algorithm computes a pointwise in frequency decomposition. We demonstrate through the use of examples that both algorithms can achieve varying levels of decomposition accuracy, and provide results that indicate the type of broadband multichannel problems that are better suited to each algorithm. It is shown that iterative methods, which generate paraunitary eigenvectors, are suited for general applications with a low number of sensors, while a DFT-based approach is useful for fixed, finite order decompositions with a small number of lags.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133419921","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 : 2017-12-10DOI: 10.1109/CAMSAP.2017.8313112
Fraser K. Coutts, K. Thompson, I. Proudler, Stephan Weiss
A number of algorithms capable of iteratively calculating a polynomial matrix eigenvalue decomposition (PEVD) have been introduced. The PEVD is an extension of the ordinary EVD to polynomial matrices and will diagonalise a parahermitian matrix using paraunitary operations. This paper introduces a novel restricted update approach for the sequential matrix diagonalisation (SMD) PEVD algorithm, which can be implemented with minimal impact on algorithm accuracy and convergence. We demonstrate that by using the proposed restricted update SMD (RU-SMD) algorithm instead of SMD, PEVD complexity and execution time can be significantly reduced. This reduction impacts on a number of broadband multichannel problems.
{"title":"Restricted update sequential matrix diagonalisation for parahermitian matrices","authors":"Fraser K. Coutts, K. Thompson, I. Proudler, Stephan Weiss","doi":"10.1109/CAMSAP.2017.8313112","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313112","url":null,"abstract":"A number of algorithms capable of iteratively calculating a polynomial matrix eigenvalue decomposition (PEVD) have been introduced. The PEVD is an extension of the ordinary EVD to polynomial matrices and will diagonalise a parahermitian matrix using paraunitary operations. This paper introduces a novel restricted update approach for the sequential matrix diagonalisation (SMD) PEVD algorithm, which can be implemented with minimal impact on algorithm accuracy and convergence. We demonstrate that by using the proposed restricted update SMD (RU-SMD) algorithm instead of SMD, PEVD complexity and execution time can be significantly reduced. This reduction impacts on a number of broadband multichannel problems.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123197820","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 : 2017-12-10DOI: 10.1109/CAMSAP.2017.8313076
Annie-Claude Pérez, C. Jauffret, D. Pillon
Range-only target motion analysis (ROTMA) is the topic of this paper: we focus our study on the numerical aspect and performance of the maximum likelihood estimates (MLE) for some scenarios when the noise polluting the measurements is additive and Gaussian. The performance is compared to the Cramér-Rao lower bound (CRLB).
{"title":"Performance of range-only TMA","authors":"Annie-Claude Pérez, C. Jauffret, D. Pillon","doi":"10.1109/CAMSAP.2017.8313076","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313076","url":null,"abstract":"Range-only target motion analysis (ROTMA) is the topic of this paper: we focus our study on the numerical aspect and performance of the maximum likelihood estimates (MLE) for some scenarios when the noise polluting the measurements is additive and Gaussian. The performance is compared to the Cramér-Rao lower bound (CRLB).","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133513326","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 : 2017-12-10DOI: 10.1109/CAMSAP.2017.8313111
D. Shutin, B. Vexler
This work proposes an extension of a sparse Bayesian learning with dictionary refinement (SBL-DR) algorithm for a super-resolution estimation of time-varying sparse signals. Such signals are represented as a superposition of unknown but fixed number of Dirac measures with a time-varying support; as such the signal is sparse at each moment of time yet locations of Dirac measures are allowed to vary. To recover such signals an optimization framework is proposed that combines SBL-DR techniques and a penalty term that imposes smoothness constraints on the support variations in time. In contrast to state-of-the-art approaches, which typically combine parameter estimation schemes with some tracking filters, the proposed approach leads to a single objective function that permits a joint recovery of a sparse superposition of time-varying functions (trajectories). A numerical algorithm for efficient optimization of the corresponding cost function is proposed and analyzed; its performance is compared to a Kalman Enhanced Superresolution Tracking algorithm on an example of estimating parameters of time-varying multipath channels.
{"title":"Sparse Bayesian learning with dictionary refinement for super-resolution through time","authors":"D. Shutin, B. Vexler","doi":"10.1109/CAMSAP.2017.8313111","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313111","url":null,"abstract":"This work proposes an extension of a sparse Bayesian learning with dictionary refinement (SBL-DR) algorithm for a super-resolution estimation of time-varying sparse signals. Such signals are represented as a superposition of unknown but fixed number of Dirac measures with a time-varying support; as such the signal is sparse at each moment of time yet locations of Dirac measures are allowed to vary. To recover such signals an optimization framework is proposed that combines SBL-DR techniques and a penalty term that imposes smoothness constraints on the support variations in time. In contrast to state-of-the-art approaches, which typically combine parameter estimation schemes with some tracking filters, the proposed approach leads to a single objective function that permits a joint recovery of a sparse superposition of time-varying functions (trajectories). A numerical algorithm for efficient optimization of the corresponding cost function is proposed and analyzed; its performance is compared to a Kalman Enhanced Superresolution Tracking algorithm on an example of estimating parameters of time-varying multipath channels.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123546893","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 : 2017-12-10DOI: 10.1109/CAMSAP.2017.8313200
Tzvi Diskin, Gordana Drašković, F. Pascal, A. Wiesel
In this paper, we consider the use of deep neural networks in the context of robust regression. We address the standard linear model with observations that are corrupted by outliers. We build upon Huber's robust regression and the classical least trimmed squares estimator, and propose a deep neural network that generalizes both and provides high accuracy with low computational complexity. The network is trained for arbitrary linear models using a single training phase. Numerical experiments with synthetic data demonstrate that the network can handle on a large range of Signal-to-Noise Ratio (SNR) and is robust to different types of outliers.
{"title":"Deep robust regression","authors":"Tzvi Diskin, Gordana Drašković, F. Pascal, A. Wiesel","doi":"10.1109/CAMSAP.2017.8313200","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313200","url":null,"abstract":"In this paper, we consider the use of deep neural networks in the context of robust regression. We address the standard linear model with observations that are corrupted by outliers. We build upon Huber's robust regression and the classical least trimmed squares estimator, and propose a deep neural network that generalizes both and provides high accuracy with low computational complexity. The network is trained for arbitrary linear models using a single training phase. Numerical experiments with synthetic data demonstrate that the network can handle on a large range of Signal-to-Noise Ratio (SNR) and is robust to different types of outliers.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132925902","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 : 2017-12-10DOI: 10.1109/CAMSAP.2017.8313173
V. Zarzoso
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia encountered in clinical practice. Recently, a tensor decomposition approach has been put forward for noninvasive analysis of AF from surface electrocardiogram (ECG) records. Multilead ECG data are stored in tensor form and factorized via the block term decomposition (BTD). An accurate selection of parameters, including the number of block terms and the rank of the Hankel matrix factors, is necessary to guarantee physiologically significant results by this approach. The present work proposes to estimate the matrix rank by exploiting the characteristics of atrial activity during AF, which can be approximated by an autoregressive (AR) model in short records. To test this idea, three AR model order estimates are considered: Akaike's information criterion, minimum description length and partial autocorrelation function. The quality of the resulting tensor decompositions is evaluated in terms of both computational and physiologically related indices. Numerical experiments demonstrate that these model order estimation methods can find matrix rank values leading to accurate BTD approximations of the AF ECG tensor and physiologically plausible results.
{"title":"Parameter estimation in block term decomposition for noninvasive atrial fibrillation analysis","authors":"V. Zarzoso","doi":"10.1109/CAMSAP.2017.8313173","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313173","url":null,"abstract":"Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia encountered in clinical practice. Recently, a tensor decomposition approach has been put forward for noninvasive analysis of AF from surface electrocardiogram (ECG) records. Multilead ECG data are stored in tensor form and factorized via the block term decomposition (BTD). An accurate selection of parameters, including the number of block terms and the rank of the Hankel matrix factors, is necessary to guarantee physiologically significant results by this approach. The present work proposes to estimate the matrix rank by exploiting the characteristics of atrial activity during AF, which can be approximated by an autoregressive (AR) model in short records. To test this idea, three AR model order estimates are considered: Akaike's information criterion, minimum description length and partial autocorrelation function. The quality of the resulting tensor decompositions is evaluated in terms of both computational and physiologically related indices. Numerical experiments demonstrate that these model order estimation methods can find matrix rank values leading to accurate BTD approximations of the AF ECG tensor and physiologically plausible results.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130339057","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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