Related papers: Boosting Independent Component Analysis

Boosting Independent Component Analysis

URL: http://arxiv.org/abs/2112.06920v1
Date: Sun, 12 Dec 2021 14:53:42 GMT
Title: Boosting Independent Component Analysis
Authors: Yunpeng Li, ZhaoHui Ye
Abstract summary: We present a novel boosting-based algorithm for independent component analysis. Our algorithm fills the gap in the nonparametric independent component analysis by introducing boosting to maximum likelihood estimation.
Score: 5.770800671793959
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Independent component analysis is intended to recover the unknown components as independent as possible from their linear mixtures. This technique has been widely used in many fields, such as data analysis, signal processing, and machine learning. In this paper, we present a novel boosting-based algorithm for independent component analysis. Our algorithm fills the gap in the nonparametric independent component analysis by introducing boosting to maximum likelihood estimation. A variety of experiments validate its performance compared with many of the presently known algorithms.

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