TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Multilinear class-specific discriminant analysis

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut131-136
Sivumäärä6
JulkaisuPattern Recognition Letters
Vuosikerta100
DOI - pysyväislinkit
TilaJulkaistu - 1 joulukuuta 2017
OKM-julkaisutyyppiA1 Alkuperäisartikkeli

Tiivistelmä

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.