Goodman, S. and Hunter, Andrew (1999) Feature extraction algorithms for pattern classification. In: 9th International Conference on Neural Networks, 7-10 September 1999, University of Edinburgh, Edinburgh, Scotland.
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Feature extraction is often an important preprocessing step in classifier design, in order to overcome the problems associated with having a large input space. A common way of doing this is to use principle component analysis to find the most important features. However, it has been recognised that this may not produce an optimal set of features in some problems since the method relies on the second order statistics (covariance structure) of the data. In the paper a method called projection pursuit is presented, which is capable of extracting features based on higher order statistics of the distribution. The original projection pursuit algorithm performs a full d-dimensional search (where d is the number of features sought) that is impractical when d is large. Instead, a simple stepwise approach is suggested in which the computations only grow linearly with d. Some simulations on six publicly available data sets are shown which shows how it may be superior to PCA on some tasks in pattern classification
|Item Type:||Conference or Workshop Item (Paper)|
|Additional Information:||(c) 1999 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works|
|Keywords:||extraction algorithms, pattern classification|
|Subjects:||G Mathematical and Computer Sciences > G400 Computer Science|
|Divisions:||College of Science > School of Computer Science|
|Deposited By:||Rosaline Smith|
|Deposited On:||09 Jul 2010 11:36|
|Last Modified:||05 Jun 2013 15:31|
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