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Local discriminant bases and their applications. (English) Zbl 0863.94004
Summary: We describe an extension to the “best-basis” method to select an orthonormal basis suitable for signal/image classification problems from a large collection of orthonormal bases consisting of wavelet packets or local trigonometric bases. The original best-basis algorithm select a basis minimizing entropy from such a “library of orthonormal bases’ whereas the proposed algorithm selects a basis maximizing a certain discriminant measure (e.g., relative entropy) among classes. Once such a basis is selected, a small number of most significant coordinates (features) are fed into a traditional classifier such as Linear Discriminant Analysis (LDA) or Classification and Regression Tree $$(\text{CART}^{\text{TM}})$$. The performance of these statistical method is enhanced since the proposed methods reduce the dimensionality of the problem at hand without losing important information for that problem. Here, the basis functions which are well-localized in the time-frequency plane are used as feature-extractors. We applied our method to two signal classification problems and an image texture classification problem. These experiments show the superiority of our method over the direct application of these classifiers on the input signals. As a further application, we also describe a method to extract signal components from data consisting of signal and textured background.

##### MSC:
 94A12 Signal theory (characterization, reconstruction, filtering, etc.) 68W10 Parallel algorithms in computer science 62B10 Statistical aspects of information-theoretic topics 62H30 Classification and discrimination; cluster analysis (statistical aspects)
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