Speed-up and multi-view extensions to subclass discriminant analysis

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Kateryna Chumachenko, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj. Speed-up and multi-view extensions to subclass discriminant analysis. Pattern Recognition 111, (2021), 107660, ISSN 0031-3203. https://doi.org/10.1016/j.patcog.2020.107660

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Title: Speed-up and multi-view extensions to subclass discriminant analysis
Author: Chumachenko, Kateryna; Raitoharju, Jenni; Iosifidis, Alexandros; Gabbouj, Moncef
Publisher: Elsevier
Date: 2021
Language: en
Belongs to series: Pattern Recognition 111, (2021), 107660
ISSN: 0031-3203
DOI: https://doi.org/10.1016/j.patcog.2020.107660
URI: http://hdl.handle.net/10138/336825
Abstract: In this paper, we propose a speed-up approach for subclass discriminant analysis and formulate a novel efficient multi-view solution to it. The speed-up approach is developed based on graph embedding and spectral regression approaches that involve eigendecomposition of the corresponding Laplacian matrix and regression to its eigenvectors. We show that by exploiting the structure of the between-class Laplacian matrix, the eigendecomposition step can be substituted with a much faster process. Furthermore, we formulate a novel criterion for multi-view subclass discriminant analysis and show that an efficient solution to it can be obtained in a similar manner to the single-view case. We evaluate the proposed methods on nine single-view and nine multi-view datasets and compare them with related existing approaches. Experimental results show that the proposed solutions achieve competitive performance, often outperforming the existing methods. At the same time, they significantly decrease the training time.
Description: Highlights • We present a speed-up extension to Subclass Discriminant Analysis. • We propose an extension to SDA for multi-view problems and a fast solution to it. • The proposed approaches result in lower training time and competitive performance.
Subject: subclass discriminant analysis
spectral regression
multi-view learning
kernel regression
subspace learning
training time
lower training time
performance
competitive performance
Computing Sciences
113 Computer and information sciences
analysis
matrices
experimental methods
evaluation methods
evaluation
methods
graphs
Subject (ysa): koneoppiminen
tekoäly
koulutusaika
suorituskyky
tietojenkäsittelytiede
analyysi
matriisit
kokeelliset menetelmät
arviointimenetelmät
arviointi
menetelmät
graafit
Rights: CC BY 4.0


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