Supervised orthogonal discriminant projection based on double adjacency graphs for image classification

This study proposes a supervised orthogonal discriminant projection (SODP) based on double adjacency graphs (DAGs). SODP based on DAG (SODP-DAG) aims to minimise the local within-class scatter and simultaneously maximise both the local between-class scatter and the non-local scatter, where the local between-class scatter and the local within-class scatter are constructed by applying the DAG structure. By doing so, SODP-DAG can keep the local within-class structure for original data and find the optimal discriminant directions effectively. Moreover, four schemes are designed for constructing weight matrices in SODP-DAG. To validate the performance of SODP-DAG, the authors compared it with orthogonal discriminant projection, SODP and others on several publicly available datasets. Experimental results show the feasibility and effectiveness of SODP-DAG.

[1]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[2]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[3]  Honggang Zhang,et al.  Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics" , 2007, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Shuicheng Yan,et al.  Graph embedding: a general framework for dimensionality reduction , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[5]  Shuicheng Yan,et al.  Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007 .

[6]  Jian Yang,et al.  LPP solution schemes for use with face recognition , 2010, Pattern Recognit..

[7]  Xiaolong Teng,et al.  Face recognition using discriminant locality preserving projections , 2006, Image Vis. Comput..

[8]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[9]  Weiguo Gong,et al.  Null space discriminant locality preserving projections for face recognition , 2008, Neurocomputing.

[10]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[11]  Yuxiao Hu,et al.  Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  W. Wong,et al.  Supervised optimal locality preserving projection , 2012, Pattern Recognit..

[13]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[14]  Shanwen Zhang,et al.  A supervised orthogonal discriminant projection for tumor classification using gene expression data , 2013, Comput. Biol. Medicine.

[15]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[16]  Sebastian Mika,et al.  Kernel Fisher Discriminants , 2003 .

[17]  Miao Qi,et al.  A Supervised Locality Preserving Projections Based Local Matching Algorithm for Face Recognition , 2010, AST/UCMA/ISA/ACN.

[18]  Li Zhang,et al.  Double adjacency graphs-based discriminant neighborhood embedding , 2015, Pattern Recognit..

[19]  Chao Wang,et al.  Supervised feature extraction based on orthogonal discriminant projection , 2009, Neurocomputing.

[20]  Xiangyang Xue,et al.  Discriminant neighborhood embedding for classification , 2006, Pattern Recognit..