Blind identification of the underdetermined mixing matrix based on K-weighted hyperline clustering

Blind identification of the underdetermined mixing matrix is an emerging problem in the area of sparse component analysis (SCA). Traditionally, the K-hyperLine clustering (K-HLC) learning algorithm is employed to solve it, but this method is designed under the strict sparse assumption on the source signals. In order to deal with the blind identification problem of multiple dominant, a discriminatory clustering algorithm, K-weighted hyperline clustering (K-WHLC), is developed via weighting scheme. The Gaussian membership function is offered as the weight factor for the hyperline clustering, together with an optimal selection in relation to the involved threshold. As shown in the paper, this discriminatory clustering scheme is efficient and especially suitable for the hyperline identification against the multiple dominant SCA problem. Also, the developed algorithm has higher accuracy than the traditional K-HLC, and it is with lower computational cost in the medium or large-scale problem. Numerical simulations are provided finally to verify the advantages of our clustering scheme.