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Feature selection for unsupervised learning

Adhikary, Jyoti Ranjan and Murty, Narasimha M (2012) Feature selection for unsupervised learning. In: ICONIP 2012 19th International Conference, November 12-15, 2012, Doha, Qatar.

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Official URL: http://dx.doi.org/10.1007/978-3-642-34487-9_47


In this paper, we present a methodology for identifying best features from a large feature space. In high dimensional feature space nearest neighbor search is meaningless. In this feature space we see quality and performance issue with nearest neighbor search. Many data mining algorithms use nearest neighbor search. So instead of doing nearest neighbor search using all the features we need to select relevant features. We propose feature selection using Non-negative Matrix Factorization(NMF) and its application to nearest neighbor search. Recent clustering algorithm based on Locally Consistent Concept Factorization(LCCF) shows better quality of document clustering by using local geometrical and discriminating structure of the data. By using our feature selection method we have shown further improvement of performance in the clustering.

Item Type: Conference Paper
Publisher: Springer Berlin Heidelberg
Additional Information: Copyright of this article belongs to Springer Berlin Heidelberg.
Keywords: Feature Selection; Non-Negative Matrix Factorization(NMF); Locally Consistent Concept Factorization(LCCF)
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 02 Jul 2013 06:45
Last Modified: 02 Jul 2013 06:45
URI: http://eprints.iisc.ac.in/id/eprint/46621

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