NDLI: Self-organizing neural networks for class-separability features

Content Provider	IEEE Xplore Digital Library
Author	Chatterjee, C. Roychowdhury, V.
Copyright Year	1996
Description	Author affiliation: Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA (Chatterjee, C.)
Abstract	We describe novel networks and self-organizing learning algorithms to extract features that are effective for preserving class separability. These are entirely different from features used for data representation such as the principal component analysis (PCA) features. The features discussed in this study are: (1) multivariate linear discriminant analysis (LDA) features in the multi-class case, and (2) linear features from the Bhattacharyya distance measure in a two-class case. Contributions in this study are: (1) we describe a novel adaptive network training algorithm that is proven to converge to the above features with probability one; (2) the training procedure is well-suited for online applications, and the network inputs are considered as a flow of data. The means and covariances of the training data are also computed adaptively; (3) we discuss two-layer linear networks to extract the LDA and Bhattacharyya distance features; (4) we prove the convergence of the two-layer networks to the respective features with probability one by stochastic approximation theory; (5) we present a new adaptive solution for the generalized eigenvectors of two correlation matrices. This is a generalization of the existing PCA algorithms; and (6) we present examples of the new networks for multi-class random data.
Starting Page	1445
Ending Page	1450
File Size	569010
Page Count	6
File Format	PDF
ISBN	0780332105
DOI	10.1109/ICNN.1996.549112
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-06-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Principal component analysis Linear discriminant analysis Data mining Feature extraction Adaptive systems Training data Computer networks Stochastic processes Approximation methods
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
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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