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A new approach to feature selection based on the Karhunen-Loeve expansion
- Source :
- Pattern Recognition. 5:335-352
- Publication Year :
- 1973
- Publisher :
- Elsevier BV, 1973.
-
Abstract
- After surveying existing feature selection procedures based upon the Karhunen-Loeve (K-L) expansion, the paper describes a new K-L technique that overcomes some of the limitations of the earlier procedures. The new method takes into account information on both the class variances and means, but lays particular emphasis on the classification potential of the latter. The results of a series of experiments concerned with the classification of real vector-electrocardiogram and artificially generated data demonstrate the advantages of the new method. They suggest that it is particularly useful for pattern recognition when combined with classification procedures based upon discriminant functions obtained by recursive least squares analysis.
- Subjects :
- Karhunen–Loève theorem
Recursive least squares filter
Series (mathematics)
business.industry
Pattern recognition
Feature selection
Class (biology)
Discriminant
Artificial Intelligence
Signal Processing
Pattern recognition (psychology)
Computer Vision and Pattern Recognition
Artificial intelligence
business
Software
Mathematics
Subjects
Details
- ISSN :
- 00313203
- Volume :
- 5
- Database :
- OpenAIRE
- Journal :
- Pattern Recognition
- Accession number :
- edsair.doi...........3490169a50fbc06d144ebf073643125b