Lei Sun†, Badong Chen‡, Kar-Ann Toh⋆, Zhiping Lin+

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A Parameter-Free Cauchy-Schwarz Information Measure for Independent Component Analysis Lei Sun†, Badong Chen‡, Kar-Ann Toh⋆, Zhiping Lin+ † Beijing Institute of Technology, Beijing 100081, PR China. (e-mail: sunlei@bit.edu.cn) ‡ Xi’an Jiaotong University, Xi’an 710049, PR China. ⋆ Yonsei University, Seoul 120-749, Republic of Korea. + Nanyang Technological University,Singapore 639798, Singapore. Abstract Independent component analysis (ICA) by an information measure has seen wide applications in engineering. Different from traditional probability density function based in formation measures, a probability survival distribution based Cauchy-Schwarz information measure for multiple variables is proposed in this paper. Empirical estimation of survival distribution is parameter-free which is inherited by the estimation of the new information measure. This measure is proved to be a valid statistical independence measure and is adopted as an objective function to develop an ICA algorithm which is validated by an experiment. This work shows promising potential regarding the use of survival distribution based information measure for ICA. Method Experiments Outline Parameter-free survival distribution estimation Survival Cauchy-Schwarz Mutual Information (SCS-MI) is a valid Statistical Independence measure An empirical SCS-MI estimator for multiple variables Proposed ICA algorithm based on SCS-MI estimator Experimental result and conclusion Algorithm Conclusion References [1] B. Chen, P. Zhu, and J. C. Principe, “Survival information potential: a new criterion for adaptive system train- ing,” IEEE Trans. on Signal Processing, vol. 60, no. 3, pp. 1184–1194, 2012. [2] B. Chen, N. Zheng, and J.C. Principe, “Survival k- ernel with application to kernel adaptive filtering,” in The 2013 International Joint Conference on Neural Net- works. IEEE, 2013, pp. 1–6.