A Sparse Component Analysis Algorithm Based on Finite-Mixture-Model Learning

In this paper, a finite-mixture-model learning bused sparse component analysis (SCA) algorithm is proposed. In this algorithm, a finite-mixture-model learning method is applied for estimating the mixing matrix for SCA. The main advantage of this method is the ability of selecting the number of sourc...

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Bibliographic Details
Published in2007 IEEE International Conference on Integration Technology pp. 112 - 116
Main Authors Jianzhao Qin, Zhi Wang, Hanqing Hu, Jun Cheng, Xinyu Wu, Yangsheng Xu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2007
Subjects
Algorithm design and analysis
Clustering algorithms
Eigenvalues and eigenfunctions
Independent component analysis
Information analysis
Learning systems
Matrix decomposition
Sparse matrices
Wavelet analysis
Wavelet packets
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Summary:In this paper, a finite-mixture-model learning bused sparse component analysis (SCA) algorithm is proposed. In this algorithm, a finite-mixture-model learning method is applied for estimating the mixing matrix for SCA. The main advantage of this method is the ability of selecting the number of sources and measuring reliability of the columns of the estimated mixing matrix. That is, it can give us a probability measurement of the recovered sources, which help us to determine which recovered sources are more reliable and significant. The simulation results show the effectiveness of this algorithm.
ISBN:1424410916
9781424410910
DOI:10.1109/ICITECHNOLOGY.2007.4290442