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Machine Learning for Signal Processing

MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION

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Authors:
Afshin Abdi, Faramarz Fekri
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5 March 2017 - 1:36pm
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[1] Afshin Abdi, Faramarz Fekri, "MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1638. Accessed: Nov. 22, 2017.
@article{1638-17,
url = {http://sigport.org/1638},
author = {Afshin Abdi; Faramarz Fekri },
publisher = {IEEE SigPort},
title = {MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION},
year = {2017} }
TY - EJOUR
T1 - MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION
AU - Afshin Abdi; Faramarz Fekri
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1638
ER -
Afshin Abdi, Faramarz Fekri. (2017). MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION. IEEE SigPort. http://sigport.org/1638
Afshin Abdi, Faramarz Fekri, 2017. MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION. Available at: http://sigport.org/1638.
Afshin Abdi, Faramarz Fekri. (2017). "MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION." Web.
1. Afshin Abdi, Faramarz Fekri. MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1638

A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES


Affect prediction is a classical problem and has recently garnered special interest in multimedia applications. Affect prediction in movies is one such domain, potentially aiding the design as well as the impact analysis of movies.Given the large diversity in movies (such as different genres and languages), obtaining a comprehensive movie dataset for modeling affect is challenging while models trained on smaller datasets may not generalize. In this paper, we address the problem of continuous affect ratings with the availability of limited in-domain data resources.

SigPort.zip

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Authors:
Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan
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1 March 2017 - 9:17am
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[1] Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan, "A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1554. Accessed: Nov. 22, 2017.
@article{1554-17,
url = {http://sigport.org/1554},
author = {Sabyasachee Baruah; Rahul Gupta; Shrikanth Narayanan },
publisher = {IEEE SigPort},
title = {A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES},
year = {2017} }
TY - EJOUR
T1 - A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES
AU - Sabyasachee Baruah; Rahul Gupta; Shrikanth Narayanan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1554
ER -
Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan. (2017). A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES. IEEE SigPort. http://sigport.org/1554
Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan, 2017. A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES. Available at: http://sigport.org/1554.
Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan. (2017). "A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES." Web.
1. Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan. A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1554

Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification

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28 February 2017 - 4:29am
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[1] , "Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1492. Accessed: Nov. 22, 2017.
@article{1492-17,
url = {http://sigport.org/1492},
author = { },
publisher = {IEEE SigPort},
title = {Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification},
year = {2017} }
TY - EJOUR
T1 - Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1492
ER -
. (2017). Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification. IEEE SigPort. http://sigport.org/1492
, 2017. Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification. Available at: http://sigport.org/1492.
. (2017). "Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification." Web.
1. . Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1492

Fast Exemplar Selection Algorithm for Matrix Approximation and Representation: A Variant oASIS Algorithm


Extracting inherent patterns from large data using decompositions of
data matrix by a sampled subset of exemplars has found many applications
in machine learning. We propose a computationally efficient
algorithm for adaptive exemplar sampling, called fast exemplar selection
(FES). The proposed algorithm can be seen as an efficient
variant of the oASIS algorithm (Patel et al). FES iteratively selects incoherent
exemplars based on the exemplars that are already sampled.
This is done by ensuring that the selected exemplars forms a positive

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Authors:
Pulkit Sharma, Anil Kumar Sao
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28 February 2017 - 12:26am
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[1] Pulkit Sharma, Anil Kumar Sao, "Fast Exemplar Selection Algorithm for Matrix Approximation and Representation: A Variant oASIS Algorithm", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1474. Accessed: Nov. 22, 2017.
@article{1474-17,
url = {http://sigport.org/1474},
author = {Pulkit Sharma; Anil Kumar Sao },
publisher = {IEEE SigPort},
title = {Fast Exemplar Selection Algorithm for Matrix Approximation and Representation: A Variant oASIS Algorithm},
year = {2017} }
TY - EJOUR
T1 - Fast Exemplar Selection Algorithm for Matrix Approximation and Representation: A Variant oASIS Algorithm
AU - Pulkit Sharma; Anil Kumar Sao
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1474
ER -
Pulkit Sharma, Anil Kumar Sao. (2017). Fast Exemplar Selection Algorithm for Matrix Approximation and Representation: A Variant oASIS Algorithm. IEEE SigPort. http://sigport.org/1474
Pulkit Sharma, Anil Kumar Sao, 2017. Fast Exemplar Selection Algorithm for Matrix Approximation and Representation: A Variant oASIS Algorithm. Available at: http://sigport.org/1474.
Pulkit Sharma, Anil Kumar Sao. (2017). "Fast Exemplar Selection Algorithm for Matrix Approximation and Representation: A Variant oASIS Algorithm." Web.
1. Pulkit Sharma, Anil Kumar Sao. Fast Exemplar Selection Algorithm for Matrix Approximation and Representation: A Variant oASIS Algorithm [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1474

Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision


This work investigates the parameter estimation performance of super-resolution line spectral estimation using atomic norm minimization. The focus is on analyzing the algorithm's accuracy of inferring the frequencies and complex magnitudes from noisy observations. When the Signal-to-Noise Ratio is reasonably high and the true frequencies are separated by $O(\frac{1}{n})$, the atomic norm estimator is shown to localize the correct number of frequencies, each within a neighborhood of size $O(\sqrt{\frac{\log n}{n^3}} \sigma)$ of one of the true frequencies.

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10 December 2016 - 3:39pm
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[1] , "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1383. Accessed: Nov. 22, 2017.
@article{1383-16,
url = {http://sigport.org/1383},
author = { },
publisher = {IEEE SigPort},
title = {Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision},
year = {2016} }
TY - EJOUR
T1 - Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1383
ER -
. (2016). Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. IEEE SigPort. http://sigport.org/1383
, 2016. Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. Available at: http://sigport.org/1383.
. (2016). "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision." Web.
1. . Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1383

Minimum-Volume Weighted Symmetric Nonnegative Matrix Factorization for Clustering


In recent years, nonnegative matrix factorization (NMF) attracts much attention in machine learning and signal processing fields due to its interpretability of data in a low dimensional subspace. For clustering problems, symmetric nonnegative matrix factorization (SNMF) as an extension of NMF factorizes the similarity matrix of data points directly and outperforms NMF when dealing with nonlinear data structure. However, the clustering results of SNMF is very sensitive to noisy data.

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Authors:
Tianxiang Gao, Sigurdur Olafsson, Songtao Lu
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6 December 2016 - 7:16pm
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[1] Tianxiang Gao, Sigurdur Olafsson, Songtao Lu, "Minimum-Volume Weighted Symmetric Nonnegative Matrix Factorization for Clustering", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1378. Accessed: Nov. 22, 2017.
@article{1378-16,
url = {http://sigport.org/1378},
author = {Tianxiang Gao; Sigurdur Olafsson; Songtao Lu },
publisher = {IEEE SigPort},
title = {Minimum-Volume Weighted Symmetric Nonnegative Matrix Factorization for Clustering},
year = {2016} }
TY - EJOUR
T1 - Minimum-Volume Weighted Symmetric Nonnegative Matrix Factorization for Clustering
AU - Tianxiang Gao; Sigurdur Olafsson; Songtao Lu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1378
ER -
Tianxiang Gao, Sigurdur Olafsson, Songtao Lu. (2016). Minimum-Volume Weighted Symmetric Nonnegative Matrix Factorization for Clustering. IEEE SigPort. http://sigport.org/1378
Tianxiang Gao, Sigurdur Olafsson, Songtao Lu, 2016. Minimum-Volume Weighted Symmetric Nonnegative Matrix Factorization for Clustering. Available at: http://sigport.org/1378.
Tianxiang Gao, Sigurdur Olafsson, Songtao Lu. (2016). "Minimum-Volume Weighted Symmetric Nonnegative Matrix Factorization for Clustering." Web.
1. Tianxiang Gao, Sigurdur Olafsson, Songtao Lu. Minimum-Volume Weighted Symmetric Nonnegative Matrix Factorization for Clustering [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1378

Recurrent neural networks for polyphonic sound event detection in real life recordings


RECURRENT NEURAL NETWORKS FOR POLYPHONIC SOUND EVENT DETECTION IN REAL LIFE RECORDINGS

Slides from the presentation held at ICASSP 2016 for the paper: Recurrent neural networks for polyphonic sound event detection in real life recordings

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Authors:
Heikki Huttunen, Tuomas Virtanen
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4 April 2016 - 9:45am
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[1] Heikki Huttunen, Tuomas Virtanen, "Recurrent neural networks for polyphonic sound event detection in real life recordings", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1082. Accessed: Nov. 22, 2017.
@article{1082-16,
url = {http://sigport.org/1082},
author = {Heikki Huttunen; Tuomas Virtanen },
publisher = {IEEE SigPort},
title = {Recurrent neural networks for polyphonic sound event detection in real life recordings},
year = {2016} }
TY - EJOUR
T1 - Recurrent neural networks for polyphonic sound event detection in real life recordings
AU - Heikki Huttunen; Tuomas Virtanen
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1082
ER -
Heikki Huttunen, Tuomas Virtanen. (2016). Recurrent neural networks for polyphonic sound event detection in real life recordings. IEEE SigPort. http://sigport.org/1082
Heikki Huttunen, Tuomas Virtanen, 2016. Recurrent neural networks for polyphonic sound event detection in real life recordings. Available at: http://sigport.org/1082.
Heikki Huttunen, Tuomas Virtanen. (2016). "Recurrent neural networks for polyphonic sound event detection in real life recordings." Web.
1. Heikki Huttunen, Tuomas Virtanen. Recurrent neural networks for polyphonic sound event detection in real life recordings [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1082

FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION


Examples of subband filters learned using ConvRBM: (a) filters in time-domain (i.e., impulse responses), (b) filters in frequency-domain (i.e., frequency responses).

Convolutional Restricted Boltzmann Machine (ConvRBM) as a model for speech signal is presented in this paper. We have
developed ConvRBM with sampling from noisy rectified linear units (NReLUs). ConvRBM is trained in an unsupervised way to model speech signal of arbitrary lengths. Weights of the model can represent an auditory-like filterbank. Our

poster.pdf

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Authors:
Hardik B. Sailor, Hemant A. Patil
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31 March 2016 - 4:04am
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[1] Hardik B. Sailor, Hemant A. Patil, "FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1075. Accessed: Nov. 22, 2017.
@article{1075-16,
url = {http://sigport.org/1075},
author = {Hardik B. Sailor; Hemant A. Patil },
publisher = {IEEE SigPort},
title = {FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION},
year = {2016} }
TY - EJOUR
T1 - FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION
AU - Hardik B. Sailor; Hemant A. Patil
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1075
ER -
Hardik B. Sailor, Hemant A. Patil. (2016). FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION. IEEE SigPort. http://sigport.org/1075
Hardik B. Sailor, Hemant A. Patil, 2016. FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION. Available at: http://sigport.org/1075.
Hardik B. Sailor, Hemant A. Patil. (2016). "FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION." Web.
1. Hardik B. Sailor, Hemant A. Patil. FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1075

FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION


Examples of subband filters learned using ConvRBM: (a) filters in time-domain (i.e., impulse responses), (b) filters in frequency-domain (i.e., frequency responses).

Convolutional Restricted Boltzmann Machine (ConvRBM) as a model for speech signal is presented in this paper. We have
developed ConvRBM with sampling from noisy rectified linear units (NReLUs). ConvRBM is trained in an unsupervised way to model speech signal of arbitrary lengths. Weights of the model can represent an auditory-like filterbank. Our

poster.pdf

PDF icon poster.pdf (413 downloads)

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Authors:
Hardik B. Sailor, Hemant A. Patil
Submitted On:
31 March 2016 - 4:04am
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poster.pdf

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[1] Hardik B. Sailor, Hemant A. Patil, "FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1074. Accessed: Nov. 22, 2017.
@article{1074-16,
url = {http://sigport.org/1074},
author = {Hardik B. Sailor; Hemant A. Patil },
publisher = {IEEE SigPort},
title = {FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION},
year = {2016} }
TY - EJOUR
T1 - FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION
AU - Hardik B. Sailor; Hemant A. Patil
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1074
ER -
Hardik B. Sailor, Hemant A. Patil. (2016). FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION. IEEE SigPort. http://sigport.org/1074
Hardik B. Sailor, Hemant A. Patil, 2016. FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION. Available at: http://sigport.org/1074.
Hardik B. Sailor, Hemant A. Patil. (2016). "FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION." Web.
1. Hardik B. Sailor, Hemant A. Patil. FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1074

Orthogonal Sparse Eigenvectors: A Procrustes Problem


The problem of estimating sparse eigenvectors of a symmetric matrix attracts a lot of attention in many applications, especially those with high dimensional data set. While classical eigenvectors can be obtained as the solution of a maximization

SPCAposter.pdf

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Authors:
Konstantinos Benidis, Ying Sun, Prabhu Babu, Daniel P. Palomar
Submitted On:
22 March 2016 - 2:22am
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[1] Konstantinos Benidis, Ying Sun, Prabhu Babu, Daniel P. Palomar, "Orthogonal Sparse Eigenvectors: A Procrustes Problem", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/954. Accessed: Nov. 22, 2017.
@article{954-16,
url = {http://sigport.org/954},
author = {Konstantinos Benidis; Ying Sun; Prabhu Babu; Daniel P. Palomar },
publisher = {IEEE SigPort},
title = {Orthogonal Sparse Eigenvectors: A Procrustes Problem},
year = {2016} }
TY - EJOUR
T1 - Orthogonal Sparse Eigenvectors: A Procrustes Problem
AU - Konstantinos Benidis; Ying Sun; Prabhu Babu; Daniel P. Palomar
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/954
ER -
Konstantinos Benidis, Ying Sun, Prabhu Babu, Daniel P. Palomar. (2016). Orthogonal Sparse Eigenvectors: A Procrustes Problem. IEEE SigPort. http://sigport.org/954
Konstantinos Benidis, Ying Sun, Prabhu Babu, Daniel P. Palomar, 2016. Orthogonal Sparse Eigenvectors: A Procrustes Problem. Available at: http://sigport.org/954.
Konstantinos Benidis, Ying Sun, Prabhu Babu, Daniel P. Palomar. (2016). "Orthogonal Sparse Eigenvectors: A Procrustes Problem." Web.
1. Konstantinos Benidis, Ying Sun, Prabhu Babu, Daniel P. Palomar. Orthogonal Sparse Eigenvectors: A Procrustes Problem [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/954

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