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Graph-Based Multilevel Dimensionality Reduction with Applications to Eigenfaces and Latent Semantic Indexing

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3 Author(s)
Sakellaridi, S. ; Comp. Sci. & Eng. Dept., Univ. of Minnesota Minneapolis, Minneapolis, MN, USA ; Haw-ren Fang ; Saad, Y.

Dimension reduction techniques have been successfully applied to face recognition and text information retrieval. The process can be 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.

Published in:

Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference on

Date of Conference:

11-13 Dec. 2008