Abstract
The underdetermined blind source separation problem is a common problem in our daily life, but it is difficult to solve because of its underdetermined. In the literature, sparse component analysis which exploits the sparsity of sources in a predefined sparse dictionary has been proposed to solve it. Usually, sparse component analysis uses a two-stage approach. The first stage is to estimate the mixing matrix and the second stage is to reconstruct sources. In fact, the second stage is a sparse optimization problem. In this paper, we model the problem of reconstructing sources as a bi-objective optimization problem. We take the error and sparsity as the two optimization objectives, and propose an iterative algorithm based on mixed integer programming to solve the bi-objective source reconstructing problem. Experimental results show the accuracy and effectiveness of our proposed algorithm.
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Fan, X., Shao, L. (2016). Blind Source Separation Based on Mixed Integer Programming. In: Jia, Y., Du, J., Zhang, W., Li, H. (eds) Proceedings of 2016 Chinese Intelligent Systems Conference. CISC 2016. Lecture Notes in Electrical Engineering, vol 405. Springer, Singapore. https://doi.org/10.1007/978-981-10-2335-4_55
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DOI: https://doi.org/10.1007/978-981-10-2335-4_55
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