NDLI: Document Classification with Unsupervised Nonnegative Matrix Factorization and Supervised Percetron Learning

Content Provider	IEEE Xplore Digital Library
Author	Barman, P.C. Soo-Young Lee
Copyright Year	2007
Description	Author affiliation: Korea Adv. Inst. of Sci. & Technol., Daejeon (Barman, P.C.)
Abstract	A new hybrid neural network model is proposed for the document classification. The NMF-SLP model consists of 2 layers, in which the first non-negative matrix factorization (NMF) layer decomposes a document into several clusters, and the second single-layer-perceptron (SLP) layer classifies the document based on the clusters. The NMF layer is trained by factorizing the document word frequency matrix into feature matrix and coefficient matrix, and then estimating the pseudo-inverse of the feature matrix. The SLP layer is trained by standard error minimization algorithm. Classification performances are investigated as a function of the cluster number, i.e., the number of hidden neurons, and also slope of sigmoidal nonlinearity at the hidden neurons. The developed model demonstrates much better classification accuracy compared to the simple NMF and k-NN classifiers, while standard multi-layer Perceptron is almost impractical to train properly due to high dimensional inputs and large number of adaptive elements.
Starting Page	182
Ending Page	186
File Size	1389633
Page Count	5
File Format	PDF
ISBN	1424412196
DOI	10.1109/ICIA.2007.4295722
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-08
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Transmission line matrix methods Neurons Supervised learning Neural networks Clustering algorithms Cities and towns Feature extraction Frequency estimation Matrix decomposition Unsupervised learning
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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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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