TY - GEN
T1 - Charrelation matrix based ICA
AU - Slapak, Alon
AU - Yeredor, Arie
N1 - Funding Information:
This research was supported by the Israeli Science Foundation (grant No. 1255/08).
PY - 2012
Y1 - 2012
N2 - Charrelation matrices are a generalization of the covariance matrix, encompassing statistical information beyond second order while maintaining a convenient 2-dimensional structure. In the context of ICA, charrelation matrices-based separation was recently shown to potentially attain superior performance over commonly used methods. However, this approach is strongly dependent on proper selection of the parameters (termed processing-points) which parameterize the charrelation matrices. In this work we derive a data-driven criterion for proper selection of the set of processing-points. The proposed criterion uses the available mixtures samples to quantify the resulting separation errors' covariance matrix in terms of the processing points. Minimizing the trace of this matrix with respect to the processing points enables to optimize (asymptotically) the selection of these points, thereby yielding better separation results than other methods, as we demonstrate in simulation.
AB - Charrelation matrices are a generalization of the covariance matrix, encompassing statistical information beyond second order while maintaining a convenient 2-dimensional structure. In the context of ICA, charrelation matrices-based separation was recently shown to potentially attain superior performance over commonly used methods. However, this approach is strongly dependent on proper selection of the parameters (termed processing-points) which parameterize the charrelation matrices. In this work we derive a data-driven criterion for proper selection of the set of processing-points. The proposed criterion uses the available mixtures samples to quantify the resulting separation errors' covariance matrix in terms of the processing points. Minimizing the trace of this matrix with respect to the processing points enables to optimize (asymptotically) the selection of these points, thereby yielding better separation results than other methods, as we demonstrate in simulation.
UR - http://www.scopus.com/inward/record.url?scp=84857328021&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-28551-6_14
DO - 10.1007/978-3-642-28551-6_14
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AN - SCOPUS:84857328021
SN - 9783642285509
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 107
EP - 114
BT - Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings
T2 - 10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012
Y2 - 12 March 2012 through 15 March 2012
ER -