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ICANN
2005
Springer

Handwritten Digit Recognition with Nonlinear Fisher Discriminant Analysis

11 years 3 months ago
Handwritten Digit Recognition with Nonlinear Fisher Discriminant Analysis
Abstract. To generalize the Fisher Discriminant Analysis (FDA) algorithm to the case of discriminant functions belonging to a nonlinear, finite dimensional function space F (Nonlinear FDA or NFDA), it is sufficient to expand the input data by computing the output of a basis of F when applied to it [1–4]. The solution to NFDA can then be found like in the linear case by solving a generalized eigenvalue problem on the between- and within-classes covariance matrices (see e.g. [5]). The goal of NFDA is to find linear projections of the expanded data (i.e., nonlinear transformations of the original data) that minimize the variance within a class and maximize the variance between different classes. Such a representation is of course ideal to perform classification. The application of NFDA to pattern recognition is particularly appealing, because for a given input signal and a fixed function space it has no parameters and it is easy to implement and apply. Moreover, given C classes on...
Pietro Berkes
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ICANN
Authors Pietro Berkes
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