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Deep-RLS: A Model-Inspired Deep Learning Approach to Nonlinear PCA
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2020
arXiv
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In particular, we formulate the nonlinear PCA for the blind source separation (BSS) problem and show through numerical analysis that Deep-RLS results in a significant improvement in the accuracy of recovering ...
In this work, we consider the application of model-based deep learning in nonlinear principal component analysis (PCA). ...
to improve the recursive least squares solution for nonlinear PCA. ...
arXiv:2011.07458v2
fatcat:xwnxgzu7vvai3i2cgparlqzbq4
A class of neural networks for independent component analysis
1997
IEEE Transactions on Neural Networks
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We consider learning algorithms for each layer, and modify our previous nonlinear PCA type algorithms so that their separation capabilities are greatly improved. ...
The basic ICA network consists of whitening, separation, and basis vector estimation layers. It can be used for both blind source separation and estimation of the basis vectors of ICA. ...
ACKNOWLEDGMENT The authors are grateful to the reviewers for their detailed and useful comments. ...
doi:10.1109/72.572090
pmid:18255654
fatcat:7ycqgkg5yvdhflruafe75wfaay
On the Relationships between Blind Equalization and Blind Source Separation – Part II: Foundations
2007
Journal of Communication and Information Systems
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However, in this second part, equivalences between the Benveniste-Goursat-Ruget theorem and the approach to blind source separation based on maximum-likelihood, between the Shalvi-Weinstein techniques ...
The objective of this two-part work is to present and discuss the relationships between the problems of blind equalization and blind source separation. ...
ACKNOWLEDGEMENTS The authors thank FAPESP and CNPq for the financial support. Our warmest thanks go to Dr. ...
doi:10.14209/jcis.2007.6
fatcat:nthkl4pexjhbrowrb6whj2ig5a
A Review of Independent Component Analysis Techniques and their Applications
2008
IETE Technical Review
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Independent Component Analysis, a computationally efficient blind statistical signal processing technique, has been an area of interest for researchers for many practical applications in various fields ...
In this work, we present different ICA algorithms from their basics to their potential applications to serve as a comprehensive single source for an inquisitive researcher to carry out his work in this ...
GA has been used for nonlinear blind source separation in [29, 30] and for noise separation from electrocardiogram signals in [31] . ...
doi:10.4103/0256-4602.45424
fatcat:dq3m4vngxvehhgjojpi52or6ly
New blind estimation method of evoked potentials based on minimum dispersion criterion and fractional lower order statistics
2008
Journal of Biomedical Science and Engineering
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Conventional blind separation and estimation method of evoked potentials is based on second order statistics (SOS). ...
Conventional blind separation and estimation method of evoked potentials is based on second order statistics or high order Statistics. ...
Conventional blind separation and estimation method of evoked potentials is based on second order statistics. ...
doi:10.4236/jbise.2008.12015
fatcat:h7zcjpnsszcr7d6brlgnrfwrzy
Second Order Hebbian Neural Networks And Blind Source Separation
1998
Zenodo
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In particular, Section 2 reviews the use of second order statistics for the blind separation of uncorrelated sources based on similar work proposed for digital communications problems. ...
If one uses methods based on higher-order statistics (HOS) one assumes that the source samples are independent random variables and that their pdf's are not Gaussian except for perhaps only one source ...
doi:10.5281/zenodo.36539
fatcat:jjtv3oy3n5fuzirveaqbruox7a
Harmonic Elimination of Inverters using Blind Signal Separation
2005
American Journal of Applied Sciences
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The harmonic separation process is implemented with a processor achieves low THD using Blind Signal separation. It is mostly used in medical instrumentation and medical applications like ECG, EEG. ...
With the rapid growth in the utilization of the rectifier for critical loads, e.g. In computer or medical equipment the need for high quality uninterruptible power is increasing. ...
Linear models of the form (1) are used in several known techniques, but the assumptions are different .In the standard least square method, that the matrix A is completely known. ...
doi:10.3844/ajassp.2005.1434.1437
fatcat:wocuz6r6efczfo67rla4vdpsca
A Novel Blind Source Separation Algorithm using Bussgang Criterion and Natural Gradient
2016
Indian Journal of Science and Technology
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Objectives: In this paper, a novel algorithm based on Transform Domain Least Mean Square (TDLMS) is proposed for Blind Source Separation (BSS). ...
The natural gradient is computed from a cost function based on Bussgang criterion. ...
Vishnu Priye, Dean, R&D, ISM Dhanbad, for the fruitful discussions and help in writing this paper.
References ...
doi:10.17485/ijst/2016/v9i39/100365
fatcat:lazrfnfpyvgllophdfoayd2tk4
Neural Network Implementations for PCA and Its Extensions
2012
ISRN Artificial Intelligence
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PCA is a statistical method that is directly related to EVD and SVD. Minor component analysis (MCA) is a variant of PCA, which is useful for solving total least squares (TLSs) problems. ...
These methods are useful in adaptive signal processing, blind signal separation (BSS), pattern recognition, and information compression. ...
In Section 5, least means squared error-based PCA methods are dealt with. Other optimization-based PCA methods are described in Section 6. PCA based on the anti-Hebbian rule is treated in Section 7. ...
doi:10.5402/2012/847305
fatcat:5v5l5v56ozg7lkxfktm5t7cgle
Page 23 of The Journal of Neuroscience Vol. 19, Issue 7
[page]
1999
The Journal of Neuroscience
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PCA-based decomposition methods
A second class of proposed LPC decompositions have involved PCA (Donchin, 1966; Glaser and Ruchkin, 1976; Friedman, 1984; Dien et al., 1997). ...
The current implementation limits the number of data channels (and separated sources) that can be practically separated to ~50 on current computers. ...
Independent Component Analysis and Blind Signal Separation: Theory, Algorithms and Applications
2012
Learning and Nonlinear Models
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An overview on the main statistical principles that guide the search for the independent components is formulated, methods for blind signal separation that require both high-order and second-order statistics ...
This paper reviews Independent Components Analysis (ICA) and Blind Signal Separation (BSS) problems. ...
Acknowledgements The authors are thankful for the support provided by CNPq and FAPERJ (Brazil), and for the Brazilian Navy Research Institute (IPqM) for providing the data set used in this work. ...
doi:10.21528/lnlm-vol10-no1-art4
fatcat:fewa5i5dozbilbr3euyntf4kvu
A High Order Cumulants Based Multivariate Nonlinear Blind Source Separation Method
2005
Machine Learning
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Following the proposed nonlinear model, the blind source separation (BSS) criterion, as a function of high-order cumulants, is shown to produce a block-structured joint cumulant matrix by an orthogonal ...
The nonlinear patterns are identified by extracting their lower-dimensional manifolds via the principal curves method and then transforming back to the original data space. ...
Nonlinear blind source separation algorithm
Review on linear BSS To derive our nonlinear BSS algorithm, we first present a brief review of higher-order cumulants-based linear BSS methods. ...
doi:10.1007/s10994-005-1506-8
fatcat:tbpq7edwhncc5hdips42h2mevq
Image denoising using self-organizing map-based nonlinear independent component analysis
2002
Neural Networks
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We then detail a BSS method based on SOMs and intended for image denoising applications. ...
This paper proposes the use of self-organizing maps (SOMs) to the blind source separation (BSS) problem for nonlinearly mixed signals corrupted with multiplicative noise. ...
A common assumption of linear ICA-based methods is the absence of noise and that the number of mixtures must, at least, equal the number of sources. ...
doi:10.1016/s0893-6080(02)00081-3
pmid:12416696
fatcat:t373dcy7jjbgbi3r737zoslm6i
Generalizing Independent Component Analysis for Two Related Data Sets
2006
The 2006 IEEE International Joint Conference on Neural Network Proceedings
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We introduce in this paper methods for finding mutually corresponding dependent components from two different but related data sets in an unsupervised (blind) manner. ...
We present experimental results demonstrating the usefulness of the proposed methods both for artificially generated data and for a cryptographic problem. ...
In ICA and blind source separation (BSS) [17] , [4] , measures of statistical dependence have been developed and studied in several papers. ...
doi:10.1109/ijcnn.2006.246772
dblp:conf/ijcnn/KarhunenU06
fatcat:glik7tmcfjesfcqoi6nqeqp7kq
Extending ICA for finding jointly dependent components from two related data sets
2007
Neurocomputing
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In this paper, we introduce some methods for finding mutually corresponding dependent components from two different but related data sets in an unsupervised (blind) manner. ...
We present experimental results demonstrating the usefulness of the proposed methods both for artificially generated data and for a cryptographic problem. r 2006 Published by Elsevier B.V. J. ...
We can try to roughly diagonalize all these matrices by diagonalizing just their sum matrix Efxy T þ xgðyÞ T þ gðxÞy T g. (13) In this respect, our method resembles ICA and blind source separation (BSS ...
doi:10.1016/j.neucom.2006.10.144
fatcat:qqtmqx7z4ng3zbgw4pvjkzeqbu
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