Orthogonal discriminant local tangent space alignment

Authors:
Ying-Ke Lei;Hong-Jun Wang;Shan-Wen Zhang;Shu-Lin Wang;Zhi-Guo Ding
Affiliations:
State Key Lab. of Pulsed Power Laser Techn., Electronic Eng. Inst., Hefei, Anhui, China and Int. Computing Lab, Inst. of Int. Machines, Chinese Academy of Sciences, Hefei, Anhui, China and Dept. o ...;State Key Laboratory of Pulsed Power Laser Technology, Electronic Engineering Institute, Hefei, Anhui, China;Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China;Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China and School of Computer and Communication, Hunan University, Changsha, Hunan, China;State Key Laboratory of Pulsed Power Laser Technology, Electronic Engineering Institute, Hefei, Anhui, China
Venue:
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Year:
2010

Citing 9
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation

Neural Computation
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

SIAM Journal on Scientific Computing
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Letters: Linear local tangent space alignment and application to face recognition

Neurocomputing
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Locality sensitive discriminant analysis

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Supervised learning on local tangent space

ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Efficient and robust feature extraction by maximum margin criterion

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a novel linear subspace leaning algorithm called orthogonal discriminant local tangent space alignment (O-DLTSA) is proposed. Derived from local tangent space alignment (LTSA), O-DLTSA not only inherits the advantages of LTSA which uses local tangent space as a representation of the local geometry so as to preserve the local structure, but also makes full use of class information and orthogonal subspace to improve discriminant power. The experimental results of applying O-DLTSA to standard face databases demonstrate the effectiveness of the proposed method.