A novel formulation of independence detection based on the sample characteristic function
Document typeConference lecture
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
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A novel independence test for continuous random sequences is proposed in this paper. The test is based on seeking for coherence in a particular fixed-dimension feature space based on a uniform sampling of the sample characteristic function of the data, providing significant computational advantages over kernel methods. This feature space relates uncorrelation and independence, allowing to analyze the second order statistics as it is encountered in traditional signal processing. As a result, the possibility of utilizing well known correlation tools arises, motivating the usage of Canonical Correlation Analysis as the main tool for detecting independence. Comparative simulation results are provided using a model based on fading AWGN channels.
CitationDe Cabrera, F.; Riba, J. A novel formulation of independence detection based on the sample characteristic function. A: European Signal Processing Conference. "EUSIPCO 2018: 26th European Signal Processing Conference: Rome, Italy: September 3-7, 2018". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 2608-2612.