Generalized discriminant analysis model and its extension for facial expression recognition

Over the past few decades, numerous linear discriminant analysis based extensions are proposed for dimensionality reduction. However, most of them are developed intuitively according to specific motivations by employing various techniques. Therefore, it will be instructive to provide a unified discriminant analysis model for exploring the commonalities and differences. In this paper, we propose a generalized discriminant analysis model (GDA model) by comprehensively considering the key components in designing a discriminator in which various LDA-based methods can be unified into this framework. Thus we can get better understanding of the inherent relationship among these algorithms by interpreting them from a unified perspective.

[1]  Zheng-Zhi Wang,et al.  Center-based nearest neighbor classifier , 2007, Pattern Recognit..

[2]  Stephen Lin,et al.  Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Ravi Kothari,et al.  Fractional-Step Dimensionality Reduction , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Xuelong Li,et al.  General Tensor Discriminant Analysis and Gabor Features for Gait Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  F. N. David,et al.  LINEAR STATISTICAL INFERENCE AND ITS APPLICATION , 1967 .

[6]  G. Baudat,et al.  Generalized Discriminant Analysis Using a Kernel Approach , 2000, Neural Computation.

[7]  Hua Yu,et al.  A direct LDA algorithm for high-dimensional data - with application to face recognition , 2001, Pattern Recognit..

[8]  Calyampudi Radhakrishna Rao,et al.  Linear Statistical Inference and its Applications , 1967 .

[9]  Ying-Nong Chen,et al.  Face Recognition Using Nearest Feature Space Embedding , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[11]  R. Fisher THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[12]  Feiping Nie,et al.  Extracting the optimal dimensionality for local tensor discriminant analysis , 2009, Pattern Recognit..

[13]  Hwann-Tzong Chen,et al.  Local discriminant embedding and its variants , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  Kun Zhou,et al.  Locality Sensitive Discriminant Analysis , 2007, IJCAI.

[15]  Stan Z. Li,et al.  Performance Evaluation of the Nearest Feature Line Method in Image Classification and Retrieval , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Dao-Qing Dai,et al.  Improved discriminate analysis for high-dimensional data and its application to face recognition , 2007, Pattern Recognit..

[17]  Jian Yang,et al.  KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Wenming Zheng,et al.  Locally nearest neighbor classifiers for pattern classification , 2004, Pattern Recognit..

[19]  Masashi Sugiyama,et al.  Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis , 2007, J. Mach. Learn. Res..