Abstract
In this paper, an adaptive algorithm for linear instantaneous independent component analysis is proposed, which is is based on minimizing the mutual information contrast function. Adaptive density estimation by modified kernel density estimation is applied to estimate the unknown probability density functions as well as their first and second derivatives. Empirical comparisons with several popular algorithms confirm the efficiency of the proposed algorithm.
This work is supported by natural science foundation of Shanghai, China, No.10ZR1413000, and scientific research foundation for excellent young teachers of Shanghai, China, No.gjd09005.
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Xue, Y., Wang, Y., Han, Y. (2010). Adaptive Independent Component Analysis by Modified Kernel Density Estimation. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_29
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DOI: https://doi.org/10.1007/978-3-642-14922-1_29
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