NDLI: Multiple sparse component analysis based on subspace selective search algorithm

Content Provider	IEEE Xplore Digital Library
Author	Eqlimi, E. Makkiabadi, B.
Copyright Year	2015
Description	Author affiliation: Dept. of Med. Phys. & Biomed. Eng., Tehran Univ. of Med. Sci. (TUMS), Tehran, Iran (Eqlimi, E.; Makkiabadi, B.)
Abstract	Sparse component analysis (SCA) is an approach for linear matrix factorization in an instantaneous mixing system when the number of sensors is fewer than the number of sources. SCA assumes that matrix source (S) contains as many zeros as possible. According to the Georgiev's proof, under some nonstrict conditions on sparsity of the sources, called k-sparse component analysis (k-SCA), we are able to estimate both mixing system (A) and sparse sources (S) uniquely. This paper studies the problem of underdetermind blind identification (UBI) in order to estimate the mixing matrix A based on subspace clustering scheme with k-SCA assumptions. Most k-SCA based algorithms have been designed when there are only at most k = m - 1 active sources in each time instant, where m is the number of sensors. First, we address the issue of multiple active sources i.e. when there are L active sources (0 ≤ L ≤ m - 1). Second, we propose an algorithm for joint or continuous subspace clustering and estimating of the channels in order to design an online scenario to estimate the mixing matrix columns and detect the number of sources as the mixture vectors are received sequentially. Third, our proposed algorithm “subspace selective search” $(S^{3})$ deals with the outliers within subspace clustering process and improve the accuracy of channel estimation. Numerical simulations are reported to confirm the advantages of our UBI method.
Starting Page	550
Ending Page	554
File Size	176733
Page Count	5
File Format	PDF
e-ISBN	9781479919727
DOI	10.1109/IranianCEE.2015.7146277
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-10
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Electrical engineering Blind source separation Sparse matrices Multiple SCA Multiple Active Sources Clustering algorithms Channel estimation Selective Search K-SCA Eigenvalues and eigenfunctions Sparse Component Analysis (SCA) Underdetermind Blind Identification Subspace Clustering
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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