ICA with Sparse Connections
نویسندگان
چکیده
When applying independent component analysis (ICA), sometimes we expect that the connections between the observed mixtures and the recovered independent components (or the original sources) to be sparse, to make the interpretation easier or to reduce the model complexity. In this paper we propose natural gradient algorithms for ICA with a sparse separation matrix, as well as ICA with a sparse mixing matrix. The sparsity of the matrix is achieved by applying certain penalty functions to its entries. The properties of the penalty functions are investigated. Experimental results on both artificial data and causality discovery in financial stocks show the usefulness of the proposed methods.
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