Cluster-dependent feature selection by multiple kernel self-organizing map

Kuan Chieh Huang*, Yen-Yu Lin, Jie Zhi Cheng

*Corresponding author for this work

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

2 Scopus citations

Abstract

Motivated by the fact that data of each cluster are often well captured by distinct features, we propose a clustering approach called multiple kernel self-organizing map (MK-SOM) that integrates multiple kernel learning into the learning procedure of SOM, and carries out cluster-dependent feature selection simultaneously. MK-SOM is developed to reveal the intrinsic relation between features and clusters, and is derived with an efficient optimization procedure. The proposed approach is evaluated on two benchmark datasets, UCI and Caltech-101. The promising experimental results demonstrate its effectiveness.

Original languageEnglish
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages589-592
Number of pages4
StatePublished - 1 Dec 2012
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: 11 Nov 201215 Nov 2012

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference21st International Conference on Pattern Recognition, ICPR 2012
CountryJapan
CityTsukuba
Period11/11/1215/11/12

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  • Cite this

    Huang, K. C., Lin, Y-Y., & Cheng, J. Z. (2012). Cluster-dependent feature selection by multiple kernel self-organizing map. In ICPR 2012 - 21st International Conference on Pattern Recognition (pp. 589-592). [6460203] (Proceedings - International Conference on Pattern Recognition).