A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Analysis of sparse representation and blind source separation
Neural Computation
A Note on Stone's Conjecture of Blind Signal Separation
Neural Computation
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
Learning Overcomplete Representations
Neural Computation
A novel approach for underdetermined blind sources separation in frequency domain
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
K-EVD clustering and its applications to sparse component analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Underdetermined blind source separation based on sparse representation
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
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A novel sparse measure of signal is proposed and the efficient number of sources is estimated by the best confidence limit in this work. The observations are classified by SVM trained through samples which are constructed by direction angle of sources. And columns of the mixing matrix corresponding to clustering centers of each class are obtained based on the sum of samples belong to the same class with different weights which are adjusted adaptively. It gets out of the trap of the initial values which interfere k-mean clustering quite a lot. Furthermore, the online algorithm for estimating basis matrix is proposed for large scale samples. The shortest path method is used to recover the source signals after estimating the mixing matrix. The favorable simulations show the stability and robustness of the algorithms.