NDLI: A non-parametric approach for independent component analysis using kernel density estimation

Content Provider	IEEE Xplore Digital Library
Author	Sengupta, K. Burman, P. Sharma, R.
Copyright Year	2004
Description	Author affiliation: Algorithms Group, AuthenTec, Melbourne, FL, USA (Sengupta, K.)
Abstract	Learning using independent component analysis (ICA) has found a wide range of applications in the area of computer vision and pattern analysis, ranging from face recognition to speech separation. This paper presents a non-parametric approach to the ICA problem that is robust towards outlier effects. The algorithm, for the first time in the field of ICA, adopts an intuitive and direct approach, focusing on the very definition of independence itself; i.e. the joint probability density function (pdf) of independent sources is factorial over the marginal distributions. In the proposed algorithm, kernel density estimation is employed to approximate the underlying distributions. There are two major advantages of our algorithm. First, existing algorithms focus on learning the independent components by attempting to fulfill necessary conditions (but not sufficient) for independence. For example, the Jade algorithm attempts to approximate independence by minimizing higher order statistics, which are not robust to outliers. Comparatively, our technique is inherently robust towards outlier effects. Second, since the learning employs kernel density estimation, it is naturally free from the assumptions of source distributions (unlike the Infomax algorithm). Experimental results show that the algorithm is able to perform separation of sources in the presence of outliers, whereas existing algorithms like Jade and Infomax break down under such conditions. The results have also shown that the proposed non-parametric approach is generally source distribution independent. In addition, it is able to separate non-Gaussian zero-kurtotic signals unlike the traditional ICA algorithms like Jade and Infomax.
Sponsorship	IEEE Comput. Soc
File Size	539463
File Format	PDF
ISBN	0769521584
ISSN	10636919
DOI	10.1109/CVPR.2004.1315228
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Independent component analysis Kernel Robustness Application software Computer vision Pattern analysis Face recognition Speech analysis Probability density function Higher order statistics
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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