Graph Embedding Exploiting Subclasses

Anastasios Maronidis, Anastasios Tefas, Ioannis Pitas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Abstract

Recently, subspace learning methods for Dimensionality Reduction (DR), like Subclass Discriminant Analysis (SDA) and Clustering-based Discriminant
Analysis (CDA), which use subclass information for the discrimination between the data classes, have attracted much attention. In parallel, important work
has been accomplished on Graph Embedding (GE), which is a general framework unifying several subspace learning techniques. In this paper, GE has been
extended in order to integrate subclass discriminant information resulting to the novel Subclass Graph Embedding (SGE) framework. The kernelization of
SGE is also presented. It is shown that SGE comprises a generalization of the typical GE including subclass DR methods. In this vein, the theoretical link of SDA and CDA methods with SGE is established. The efficacy and power of the SGE has been substantiated by comparing subclass DR methods versus a diversity of unimodal methods all pertaining to the SGE framework via a series of experiments on various real-world data.
Original languageEnglish
Title of host publication2015 IEEE Symposium Series on Computational Intelligence (SSCI 2015)
Subtitle of host publicationProceedings of a meeting held 7-10 December 2015, Cape Town, South Africa
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1452-1459
Number of pages8
ISBN (Electronic)9781479975600
ISBN (Print)9781479975617
DOIs
Publication statusPublished - Apr 2016
Event2015 IEEE Symposium Series on Computational Intelligence - Cape Town, South Africa
Duration: 7 Dec 201510 Dec 2015

Conference

Conference2015 IEEE Symposium Series on Computational Intelligence
Abbreviated titleSSCI 2015
CountrySouth Africa
CityCape Town
Period7/12/1510/12/15

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