Paper Info
Title
Estimating the mixing matrix in underdetermined Sparse Component Analysis (SCA) using consecutive independent component analysis (ICA)
Abstract
One of the major problems in underdetermined Sparse Com- ponent Analysis (SCA) is the appropriate estimation of the mixing matrix, A, in the linear model x (t )= As (t), espe- cially where more than one source is active at each instant of time (It is called 'multiple dominant problem'). Most of the previous algorithms were restricted to single dominant problem in which it is assumed that at each instant, there is at most one single dominant component. Moreover, because of high computational load, all present methods for multiple dominant problem are practical only for small scale cases (By 'small scale' we mean that the average number of active sources at each instant, k, is less than 5). In this paper, we propose a new method for estimating the mixing matrix, A for the large scale multiple dominant problem in SCA. Our main idea is to convert the underdetermined SCA problem into a series of determined problems, which can be solved by well-known methods like ICA. To do this, we combine both sparsity and independence assumptions to estimate the mix- ing matrix. Our method can solve high dimension problems in which k can be relatively large (about 8).
Year
Venue
Keywords
2008
EUSIPCO
independent component analysis,source separation,sparse matrices,ica,sca,mixing matrix estimation,multiple dominant problem,sparse component analysis
Field
DocType
ISSN
Instant,Underdetermined system,Pattern recognition,Matrix (mathematics),Linear model,Sparse approximation,Algorithm,Artificial intelligence,Independent component analysis,Component analysis,Mathematics
Conference
2219-5491
Citations 
PageRank 
References 
2
0.37
8
Authors
4
Name
Order
Citations
PageRank
Javanmard, A.120.37
P. Pad2161.55
Babaie-Zadeh, M.3435.03
Christian Jutten445039.98