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- W2937047855 abstract "Similar to the deep architectures, a novel multi-layer architecture is used to extend the linear blind source separation (BSS) method to the nonlinear case in this paper. The approach approximates the nonlinearities based on a polynomial network, where the layer of our network begins with the polynomial of degree 1, up to build an output layer that can represent data with a small bias by a good approximate basis. Relying on several transformations of the input data, with higher-level representation from lower-level ones, the networks are to fulfill a mapping implicitly to the high-dimensional space. Once the polynomial networks are built, the coefficient matrix can be estimated by solving an l 1 -regularization on the coding coefficient vector. The experiment shows that the proposed approach exhibits a higher separation accuracy than the comparison algorithms." @default.
- W2937047855 created "2019-04-25" @default.
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- W2937047855 date "2019-05-01" @default.
- W2937047855 modified "2023-09-23" @default.
- W2937047855 title "Polynomial Networks Representation of Nonlinear Mixtures with Application in Underdetermined Blind Source Separation" @default.
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- W2937047855 doi "https://doi.org/10.1109/icassp.2019.8682827" @default.
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