Rival Penalized Competitive Learning based approach for discrete-valued source separation.

Y M Cheung; L Xu

doi:10.1142/s0129065700000399

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Rival Penalized Competitive Learning based approach for discrete-valued source separation.

Y M Cheung·L Xu

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Abstract

This paper presents an approach based on Rival Penalized Competitive Learning (RPCL) rules for discrete-valued source separation. In this approach, we first build a connection between the source number and the cluster number of observations. Then, we use the RPCL rule to automatically find out the correct number of clusters such that the source number is determined. Moreover, we tune the de-mixing matrix based on the cluster centers instead of the observation themselves, whereby the noise interference is considerably reduced. The experiments have shown that this new approach not only quickly and automatically determines the number of sources, but also is insensitive to the noise in performing blind source separation.

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Y M Cheung, L Xu. 2000. Rival Penalized Competitive Learning based approach for discrete-valued source separation.. https://doi.org/10.1142/s0129065700000399

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