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An effective clustering technique for feature extraction

Ramdas, V and Sridhar, V and Krishna, G (1994) An effective clustering technique for feature extraction. In: Pattern Recognition Letters, 15 (9). pp. 885-891.

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The performance of a classifier depends on the accuracy of measurement of the feature values. In this paper, we describe an approach for estimating feature values using a clustering technique. To begin with, we discuss the role of multiple versions of a pattern in pattern analysis. Specifically, we describe how clustering can be employed in pattern analysis. We follow this with a discussion on the usefulness of clustering in class-characterization. Specifically, we describe an approach to account for feature value measurement errors. The proposed approach has been applied in the context of speech recognition. Specifically, we inves- tigate the estimation of formant frequencies (feature values) that form an essential ingredient in the design of a speaker-inde- pendent digit recognition system (classifier).

Item Type: Journal Article
Publication: Pattern Recognition Letters
Publisher: Elsevier
Additional Information: The copyright of this article belongs to Elsevier.
Department/Centre: Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 04 Jul 2006
Last Modified: 19 Sep 2010 04:29
URI: http://eprints.iisc.ac.in/id/eprint/7806

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