Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
408615 | Neurocomputing | 2007 | 7 Pages |
Abstract
In this paper, linear local tangent space alignment (LLTSA), as a novel linear dimensionality reduction algorithm, is proposed. It uses the tangent space in the neighborhood of a data point to represent the local geometry, and then aligns those local tangent spaces in the low-dimensional space which is linearly mapped from the raw high-dimensional space. Since images of faces often belong to a manifold of intrinsically low dimension, we develop LLTSA algorithm for effective face manifold learning and recognition. Comprehensive comparisons and extensive experiments show that LLTSA achieves much higher recognition rates than a few competing methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Tianhao Zhang, Jie Yang, Deli Zhao, Xinliang Ge,