Independent Component Analysis by Entropy Bound Minimization
Independent Component Analysis by Entropy Bound Minimization
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DOI：
10.1109/tsp.2010.2055859
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发表时间：
2010-10-01
影响因子：
5.4
通讯作者：
Adali, Tuelay
中科院分区：
文献类型：
--
作者：
Li, Xi-Lin;Adali, Tuelay
A novel (differential) entropy estimator is introduced where the maximum entropy bound is used to approximate the entropy given the observations, and is computed using a numerical procedure thus resulting in accurate estimates for the entropy. We show that such an estimator exists for a wide class of measuring functions, and provide a number of design examples to demonstrate its flexible nature. We then derive a novel independent component analysis (ICA) algorithm that uses the entropy estimate thus obtained, ICA by entropy bound minimization (ICA-EBM). The algorithm adopts a line search procedure, and initially uses updates that constrain the demixing matrix to be orthogonal for robust performance. We demonstrate the superior performance of ICA-EBM and its ability to match sources that come from a wide range of distributions using simulated and real-world data.
