Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Discriminant Analysis with Tensor Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization
ICML '06 Proceedings of the 23rd international conference on Machine learning
Knowledge and Information Systems
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Locality Alignment
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive Component Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A convergent solution to tensor subspace learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Outlier-resisting graph embedding
Neurocomputing
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
Dual-space linear discriminant analysis for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Robust Tensor Analysis With L1-Norm
IEEE Transactions on Circuits and Systems for Video Technology
Face recognition by discriminant analysis with gabor tensor representation
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Generalization of linear discriminant analysis using Lp-norm
Pattern Recognition Letters
Linear discriminant analysis with maximum correntropy criterion
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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Linear discriminant analysis (LDA) is a well-known scheme for supervised subspace learning. It has been widely used in the applications of computer vision and pattern recognition. However, an intrinsic limitation of LDA is the sensitivity to the presence of outliers, due to using the Frobenius norm to measure the inter-class and intra-class distances. In this paper, we propose a novel rotational invariant L"1 norm (i.e., R"1 norm) based discriminant criterion (referred to as DCL"1), which better characterizes the intra-class compactness and the inter-class separability by using the rotational invariant L"1 norm instead of the Frobenius norm. Based on the DCL"1, three subspace learning algorithms (i.e., 1DL"1, 2DL"1, and TDL"1) are developed for vector-based, matrix-based, and tensor-based representations of data, respectively. They are capable of reducing the influence of outliers substantially, resulting in a robust classification. Theoretical analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed DCL"1 and its algorithms.