Sparse discriminating neighborhood preserving embedding

Authors:
Li Guo;ZhongLong Zheng;Jiong Jia;Huawen Liu;Daohong Xiang
Affiliations:
Department of Computer Science, Zhejiang Normal University, Zhejiang, China;Department of Computer Science, Zhejiang Normal University, Zhejiang, China;Department of Computer Science, Zhejiang Normal University, Zhejiang, China;Department of Computer Science, Zhejiang Normal University, Zhejiang, China;Department of Computer Science, Zhejiang Normal University, Zhejiang, China
Venue:
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Year:
2012

Citing 9
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review

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

Neural Computation
Neighborhood Preserving Embedding

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Robust Face Recognition via Sparse Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparsity preserving projections with applications to face recognition

Pattern Recognition
Neighborhood preserving projections (NPP): a novel linear dimension reduction method

ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - 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

Dimensionality reduction (DR) methods have commonly been used as a principled way to process the high-dimensional data such as face images. In this paper, a novel linear DR method called discriminating neighborhood preserving embedding (DNPE), which incorporates between-class scatter matrix and within-class scatter matrix into neighborhood preserving embedding (NPE), is proposed. It has been shown that DNPE has stronger discriminating power than NPE does. Meanwhile, this paper also proposes sparse discriminating neighborhood preserving embedding (SDNPE) based on sparse representation theory, which directly generates the weight matrix without constructing adjacency graphs. Experimental results on Yale, ORL, AR and Extended YaleB face databases verify the efficacy of the proposed methods.