论文信息 - K-Subspace clustering and its application in sparse component analysis

K-Subspace clustering and its application in sparse component analysis

The K-subspace clustering algorithm is established for sparse component analysis and overcome the difficulty that conventional SCA algorithms can not overcome. The conventional SCA algorithm can only perform single dominant SCA, can not perform multiple dominant SCA, but the proposed SCA algorithm based on K-subspace clustering can overcome this difficulty.

Zhaoshui He | Andrzej Cichocki

[1] Yuanqing Li,et al. Blind estimation of channel parameters and source components for EEG signals: a sparse factorization approach , 2006, IEEE Transactions on Neural Networks.

[2] Mineichi Kudo,et al. Performance analysis of minimum /spl lscr//sub 1/-norm solutions for underdetermined source separation , 2004, IEEE Transactions on Signal Processing.

[3] Michael Elad,et al. K-SVD and its non-negative variant for dictionary design , 2005, SPIE Optics + Photonics.

[4] Michael Zibulevsky,et al. Underdetermined blind source separation using sparse representations , 2001, Signal Process..

[5] Yuanqing Li,et al. Analysis of Sparse Representation and Blind Source Separation , 2004, Neural Computation.

[6] Daniel W. C. Ho,et al. Underdetermined blind source separation based on sparse representation , 2006, IEEE Transactions on Signal Processing.

[7] Fabian J. Theis,et al. Sparse component analysis and blind source separation of underdetermined mixtures , 2005, IEEE Transactions on Neural Networks.

[8] B. A. D. H. Brandwood. A complex gradient operator and its applica-tion in adaptive array theory , 1983 .

[9] Lucas C. Parra,et al. Convolutive blind separation of non-stationary sources , 2000, IEEE Trans. Speech Audio Process..

[10] Scott Rickard,et al. Blind separation of speech mixtures via time-frequency masking , 2004, IEEE Transactions on Signal Processing.

[11] I. I. Priwalow. Einführung in die Funktionentheorie , 1970 .