NDLI: Local Linear ICA for Mutual Information Estimation in Feature Selection

Content Provider	IEEE Xplore Digital Library
Author	Tian Lan Erdogmus, D.
Copyright Year	2005
Description	Author affiliation: Dept. of Biomed. Eng., Oregon Health & Sci. Univ., Portland, OR (Tian Lan; Erdogmus, D.)
Abstract	Mutual information is an important tool in many applications. Specifically, in classification systems, feature selection based on MI estimation between features and class labels helps to identify the most useful features directly related to the classification performance. MI estimation is extremely difficult and imprecise in high dimensional feature spaces with an arbitrary distribution. We propose a framework using ICA and sample-spacing based entropy estimators to estimate MI. In this framework, the higher dimensional MI estimation is reduced to independent multiple one-dimensional MI estimation problems. This approach is computationally efficient, however, its precision heavily relies on the results of ICA. In our previous work, we assumed the feature space has linear structure, hence linear ICA was adopted. Nevertheless, this is a weak assumption, which might not be true in many applications. Although nonlinear ICA can solve any ICA problem in theory, its complexity and the requirement of data samples restrict its application. A better trade-off between linear and non-linear ICA could be local linear ICA, which uses piecewise linear ICA to approximate the non-linear relationships among the data. In this paper, we propose that by replacing linear ICA with local linear ICA, we can get precise MI estimation without greatly increasing the computational complexity. The experiment results also substantiate this claim
Starting Page	3
Ending Page	8
File Size	324242
Page Count	6
File Format	PDF
ISBN	0780395174
DOI	10.1109/MLSP.2005.1532865
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Independent component analysis Mutual information Entropy Clustering algorithms Biomedical engineering Piecewise linear techniques Linear approximation Piecewise linear approximation Computational complexity Biomedical signal processing Feature Selection Local Linear ICA Mutual Information Entropy Estimation
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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