Graph-based multilevel dimensionality reduction with applications to eigenfaces and Latent Semantic Indexing

Sophia Sakellaridi, Haw Ren Fang, Yousef Saad

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

13 Scopus citations

Abstract

Dimension reduction techniques have been successfully applied to face recognition and text information retrieval. The process can he time-consuming when the data set is large. This paper presents a multilevel framework to reduce the size of the data set, prior to performing dimension reduction. The algorithm exploits nearest-neighbor graphs. It recursively coarsens the data by finding a maximal matching level by level. The coarsened data at the lowest level is then projected using a known linear dimensionality reduction method. The same linear mapping is performed on the original data set, and on any new test data. The methods are illustrated on two applications: Eigenfaces (face recognition) and Latent Semantic Indexing (text mining). Experimental results indicate that the multilevel techniques proposed here offer a very appealing cost to quality ratio.

Original languageEnglish (US)
Title of host publicationProceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008
Pages194-200
Number of pages7
DOIs
StatePublished - Dec 1 2008
Event7th International Conference on Machine Learning and Applications, ICMLA 2008 - San Diego, CA, United States
Duration: Dec 11 2008Dec 13 2008

Publication series

NameProceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008

Other

Other7th International Conference on Machine Learning and Applications, ICMLA 2008
CountryUnited States
CitySan Diego, CA
Period12/11/0812/13/08

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