Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
GA-facilitated classifier optimization with varying similarity measures
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Where Are Linear Feature Extraction Methods Applicable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Redundant Class-Dependence Feature Analysis Based on Correlation Filters Using FRGC2.0 Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Face Recognition Using Angular LDA and SVM Ensembles
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
A Kernel Approach for Semisupervised Metric Learning
IEEE Transactions on Neural Networks
Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data
Similarity-Based Clustering
Spherical discriminant analysis in semi-supervised speaker clustering
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Multi-modal Correlation Modeling and Ranking for Retrieval
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
A non-parametric approach to automatic change detection in MRI images of the brain
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
TAKES: a fast method to select features in the kernel space
Proceedings of the 20th ACM international conference on Information and knowledge management
Multi-view discriminant analysis
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Selective generation of Gabor features for fast face recognition on mobile devices
Pattern Recognition Letters
Regularized latent least square regression for cross pose face recognition
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Face recognition using scale-adaptive directional and textural features
Pattern Recognition
A Family of Discriminative Manifold Learning Algorithms and Their Application to Speech Recognition
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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Correlation is one of the most widely used similarity measures in machine learning like Euclidean and Mahalanobis distances. However, compared with proposed numerous discriminant learning algorithms in distance metric space, only a very little work has been conducted on this topic using correlation similarity measure. In this paper, we propose a novel discriminant learning algorithm in correlation measure space, Correlation Discriminant Analysis (CDA). In this framework, based on the definitions of within-class correlation and between-class correlation, the optimum transformation can be sought for to maximize the difference between them, which is in accordance with good classification performance empirically. Under different cases of the transformation, different implementations of the algorithm are given. Extensive empirical evaluations of CDA demonstrate its advantage over alternative methods.