Feature Selection and Clustering for HCI

by Richard O. Duda
Department of Electrical Engineering
San Jose State University

© 1996-2007 Richard O. Duda; all rights reserved*

These notes provide background on feature selection and clustering for the new NSF-sponsored course entitled Human Computer Interface Design.

We often want to recognize patterns in the signals that we get from input sensors, and other notes for this course describe some statistically-based procedures for pattern classification. The standard feature-vector model for classification assumes that one way or another the designer has identified the features upon which the classification will be based. The classifier then uses all of these features to assign a feature vector to a class.

Because the specific features are so problem specific, there is no general theory for designing an effective feature set. However, there are some useful procedures for improving the performance one can obtain with a given set of features:

Note: These topics are usually included in books on pattern recognition. Standard texts on this topic include Devijver and Kittler, Duda and Hart, and Fukunaga. Ripley is an excellent recent book with a strong statistical orientation. For a fuzzy-set approach to clustering, see Bezdek.

Last revised:10/6/97

Up to EE296I
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