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ACCV
2009
Springer

Lorentzian Discriminant Projection and Its Applications

9 years 10 months ago
Lorentzian Discriminant Projection and Its Applications
This paper develops a supervised dimensionality reduction method, Lorentzian Discriminant Projection (LDP), for discriminant analysis and classification. Our method represents the structures of sample data by a manifold, which is furnished with a Lorentzian metric tensor. Different from classic discriminant analysis techniques, LDP uses distances from points to their within-class neighbors and global geometric centroid to model a new manifold to detect the intrinsic local and global geometric structures of data set. In this way, both the geometry of a group of classes and global data structures can be learnt from the Lorentzian metric tensor. Thus discriminant analysis in the original sample space reduces to metric learning on a Lorentzian manifold. The experimental results on benchmark databases demonstrate the effectiveness of our proposed method.
Risheng Liu, Zhixun Su, Zhouchen Lin, Xiaoyu Hou
Added 25 May 2010
Updated 25 May 2010
Type Conference
Year 2009
Where ACCV
Authors Risheng Liu, Zhixun Su, Zhouchen Lin, Xiaoyu Hou
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