Minimum support ICA using order statistics. part II: performance analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind separation of instantaneous mixture of sources based on orderstatistics
IEEE Transactions on Signal Processing
Information properties of order statistics and spacings
IEEE Transactions on Information Theory
Minimum support ICA using order statistics. part II: performance analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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The minimum support ICA algorithms currently use the extreme statistics difference (also called the statistical range) for support width estimation. In this paper, we extend this method by analyzing the use of (possibly averaged) differences between the N – m + 1-th and m-th order statistics, where N is the sample size and m is a positive integer lower than N/2. Numerical results illustrate the expectation and variance of the estimators for various densities and sample sizes; theoretical results are provided for uniform densities. The estimators are analyzed from the specific viewpoint of ICA, i.e. considering that the support widths and the pdf shapes vary with demixing matrix updates.