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CVPR
2007
IEEE

Integrating Global and Local Structures: A Least Squares Framework for Dimensionality Reduction

13 years 3 months ago
Integrating Global and Local Structures: A Least Squares Framework for Dimensionality Reduction
Linear Discriminant Analysis (LDA) is a popular statistical approach for dimensionality reduction. LDA captures the global geometric structure of the data by simultaneously maximizing the between-class distance and minimizing the within-class distance. However, local geometric structure has recently been shown to be effective for dimensionality reduction. In this paper, a novel dimensionality reduction algorithm is proposed, which integrates both global and local structures. The main contributions of this paper include: (1) We present a least squares formulation for dimensionality reduction, which facilities the integration of global and local structures; (2) We design an efficient model selection scheme for the optimal integration, which balances the tradeoff between the global and local structures; and (3) We present a detailed theoretical analysis on the intrinsic relationship between the proposed framework and LDA. Our extensive experimental studies on benchmark data sets show tha...
Jianhui Chen, Jieping Ye, Qi Li
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Jianhui Chen, Jieping Ye, Qi Li
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