Name
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. |
Year | DOI | Venue |
|---|---|---|
1973 | 10.1016/0031-3203(73)90025-3 | Pattern Recognition |
Keywords | Field | DocType |
Feature selection,Linear transformation,Karhunen-Loeve expansion,Covariance matrix,Scatter matrix,Least squares fit,Discriminant function | Karhunen–Loève theorem,Feature selection,Pattern recognition,Discriminant,Artificial intelligence,Recursive least squares filter,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
5 | 4 | 0031-3203 |
Citations | PageRank | References |
44 | 15.02 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Kittler | 1 | 14346 | 1465.03 |
| Peter C. Young | 2 | 222 | 110.94 |