Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimension reduction by local principal component analysis
Neural Computation
Nonlinear canonical correlation analysis by neural networks
Neural Networks
Kernel independent component analysis
The Journal of Machine Learning Research
Using KCCA for Japanese---English cross-language information retrieval and document classification
Journal of Intelligent Information Systems
Locality preserving CCA with applications to data visualization and pose estimation
Image and Vision Computing
Statistical Consistency of Kernel Canonical Correlation Analysis
The Journal of Machine Learning Research
Journal of Cognitive Neuroscience
A Novel Method of Combined Feature Extraction for Recognition
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A new method of feature fusion and its application in image recognition
Pattern Recognition
A novel feature fusion method based on partial least squares regression
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
On the eigenspectrum of the gram matrix and the generalization error of kernel-PCA
IEEE Transactions on Information Theory
Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data
IEEE Transactions on Image Processing
Facial expression recognition using kernel canonical correlation analysis (KCCA)
IEEE Transactions on Neural Networks
Neighborhood Correlation Analysis for Semi-paired Two-View Data
Neural Processing Letters
Low-resolution face recognition: a review
The Visual Computer: International Journal of Computer Graphics
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In this paper, a new feature extraction algorithm is developed based on canonical correlation analysis (CCA), called Local Discrimination CCA (LDCCA). The method considers a combination of local properties and discrimination between different classes. Not only the correlations between sample pairs but also the correlations between samples and their local neighborhoods are taken into consideration in LDCCA. Effective class separation is achieved by maximizing local within-class correlations and minimizing local between-class correlations simultaneously. Besides, a kernel version of LDCCA (KLDCCA) is proposed to cope with nonlinear problems in experiments. The experimental results on an artificial dataset, multiple feature databases and face databases including ORL, Yale, AR validate the effectiveness of the proposed methods.