Clustering complex data with group-dependent feature selection

Yen-Yu Lin*, Tyng Luh Liu, Chiou Shann Fuh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

We describe a clustering approach with the emphasis on detecting coherent structures in a complex dataset, and illustrate its effectiveness with computer vision applications. By complex data, we mean that the attribute variations among the data are too extensive such that clustering based on a single feature representation/descriptor is insufficient to faithfully divide the data into meaningful groups. The proposed method thus assumes the data are represented with various feature representations, and aims to uncover the underlying cluster structure. To that end, we associate each cluster with a boosting classifier derived from multiple kernel learning, and apply the cluster-specific classifier to feature selection across various descriptors to best separate data of the cluster from the rest. Specifically, we integrate the multiple, correlative training tasks of the cluster-specific classifiers into the clustering procedure, and cast them as a joint constrained optimization problem. Through the optimization iterations, the cluster structure is gradually revealed by these classifiers, while their discriminant power to capture similar data would be progressively improved owing to better data labeling.

Original languageEnglish
Title of host publicationComputer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings
Pages84-97
Number of pages14
EditionPART 6
DOIs
StatePublished - 15 Nov 2010
Event11th European Conference on Computer Vision, ECCV 2010 - Heraklion, Crete, Greece
Duration: 5 Sep 201011 Sep 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 6
Volume6316 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th European Conference on Computer Vision, ECCV 2010
CountryGreece
CityHeraklion, Crete
Period5/09/1011/09/10

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  • Cite this

    Lin, Y-Y., Liu, T. L., & Fuh, C. S. (2010). Clustering complex data with group-dependent feature selection. In Computer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings (PART 6 ed., pp. 84-97). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6316 LNCS, No. PART 6). https://doi.org/10.1007/978-3-642-15567-3_7