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Multilinear class-specific discriminant analysis

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)131-136
Number of pages6
JournalPattern Recognition Letters
Volume100
DOIs
Publication statusPublished - 1 Dec 2017
Publication typeA1 Journal article-refereed

Abstract

There has been a great effort to transfer linear discriminant techniques that operate on vector data to high-order data, generally referred to as Multilinear Discriminant Analysis (MDA) techniques. Many existing works focus on maximizing the inter-class variances to intra-class variances defined on tensor data representations. However, there has not been any attempt to employ class-specific discrimination criteria for the tensor data. In this paper, we propose a multilinear subspace learning technique suitable for applications requiring class-specific tensor models. The method maximizes the discrimination of each individual class in the feature space while retains the spatial structure of the input. We evaluate the efficiency of the proposed method on two problems, i.e. facial image analysis and stock price prediction based on limit order book data.

Keywords

  • Class-specific discriminant learning, Face verification, Multilinear discriminant analysis, Stock price prediction

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