Selection of best bases for classification and regression, (with R. R. Coifman),
Proc.1994 IEEE-IMS Workshop on Information Theory and Statistics, p.51, IEEE-IMS, Oct. 1994, Alexandria, VA.
We describe extensions to the "best-basis" method to select orthonormal
bases suitable for signal classification (or regression) problems from a
collection of orthonormal bases using the relative entropy (or regression
errors). Once these bases are selected, the most significant
coordinates are fed into a traditional classifier (or regression method)
such as Linear Discriminant Analysis (LDA) or a Classification and Regression
The performance of these statistical methods is enhanced since the
proposed methods reduce the dimensionality of the problems by
using the basis functions which are well-localized in the time-frequency plane
as feature extractors.
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