An extension of Fisher's discriminant analysis for stochastic processes

Hyejin Shin

Journal of Multivariate Analysis, 2008, vol. 99, issue 6, 1191-1216

Abstract: In this paper we present a general notion of Fisher's linear discriminant analysis that extends the classical multivariate concept to situations that allow for function-valued random elements. The development uses a bijective mapping that connects a second order process to the reproducing kernel Hilbert space generated by its within class covariance kernel. This approach provides a seamless transition between Fisher's original development and infinite dimensional settings that lends itself well to computation via smoothing and regularization. Simulation results and real data examples are provided to illustrate the methodology.

Keywords: Fisher's; method; Reproducing; kernel; Hilbert; space; Functional; data (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (9)

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