Multilinear class-specific discriminant analysis

    Research output: Contribution to journalArticleScientificpeer-review

    10 Citations (Scopus)

    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.

    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

    Keywords

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

    Publication forum classification

    • Publication forum level 2

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Computer Vision and Pattern Recognition
    • Artificial Intelligence

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