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Class-specific kernel discriminant analysis based on Cholesky decomposition

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017
PublisherIEEE
Pages1141-1146
Number of pages6
ISBN (Electronic)9781509061815
DOIs
Publication statusPublished - 30 Jun 2017
Publication typeA4 Article in a conference publication
EventInternational Joint Conference on Neural Networks -
Duration: 1 Jan 1900 → …

Publication series

Name
ISSN (Electronic)2161-4407

Conference

ConferenceInternational Joint Conference on Neural Networks
Period1/01/00 → …

Abstract

In this paper we describe a method for nonlinear class-specific discriminant learning that is based on Cholesky Decomposition. We show that the optimization problem solved in Class-Specific Kernel Discriminant Analysis is equivalent to that of Low-Rank Kernel Regression using training data independent target vectors. This connection allows us to devise a new Class-Specific Kernel Discriminant Analysis method that can be trained by exploiting fast linear system approaches, like the Cholesky decomposition. We verify our analysis in publicly available verification problems designed for human action recognition.

ASJC Scopus subject areas

Publication forum classification

Field of science, Statistics Finland