On feature extraction for limited class problem
説明
The availability of the canonical discriminant analysis to a limited class problem is restricted because the number of extracted features can not be or exceed the number of classes. In order to remove the restriction, a new feature extraction technique FKL is proposed and is tested by handwritten numeral recognition experiment. While the canonical discriminant analysis maximizes the variance ratio (F-ratio), and the principal component analysis (K-L expansion) minimizes the mean square error of dimension reduction, the FKL optimizes both the F-ratio and the mean square error simultaneously. The result of experiment shows that the FKL provides the richest features in discriminating power for the limited class problem when compared with other techniques including the canonical discriminant analysis, the principal component analysis, and the orthonormal discriminant vector method (ODV).
収録刊行物
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- Proceedings of 13th International Conference on Pattern Recognition
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Proceedings of 13th International Conference on Pattern Recognition 191-194 vol.2, 1996-01-01
IEEE