Abstract
In this paper we study extraction of independent components from the instantaneous sparse mixture with additive Gaussian noise. We model the problem as a dictionary-learning-like objective function which tries to discover independent atoms and corresponding sparse mixing matrix. The objective function involves fidelity term, L1 normalization term and Negentropy term which respectively limits noise, maximizes the sparseness of mixing matrix and non-Gaussianity of each atom. An alternative iteration algorithm is proposed to solve the optimization. According to our simulation, the proposed method outperforms FastICA and K-SVD.
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Acknowledgements
This work was supported partially by the National Nature Science Foundation of China under Grant Nos. 61403053. And partially this research is funded by Chongqing Natural Science Foundation (the project No. is No. cstc2014jcyjA40018,cstc2014jcyjA40022, and cstc2014kjrcqnrc40002) and by Chongqing education committee under Grant No. KJ1500402 and KJ1500417.
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© 2016 Springer International Publishing Switzerland
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Mi, JX., Li, C., Li, C. (2016). Extraction of Independent Components from Sparse Mixture. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_4
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DOI: https://doi.org/10.1007/978-3-319-42294-7_4
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