Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to algorithms
Estimating the Intrinsic Dimension of Data with a Fractal-Based Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regularized discriminant analysis for the small sample size problem in face recognition
Pattern Recognition Letters
A well-conditioned estimator for large-dimensional covariance matrices
Journal of Multivariate Analysis
Meta-clustering of gene expression data and literature-based information
ACM SIGKDD Explorations Newsletter
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locality preserving CCA with applications to data visualization and pose estimation
Image and Vision Computing
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Locality sensitive semi-supervised feature selection
Neurocomputing
Computational Statistics & Data Analysis
An Adaptable-Multilayer Fractional Fourier Transform Approach for Image Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-view clustering via canonical correlation analysis
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Multiple view semi-supervised dimensionality reduction
Pattern Recognition
A Bootstrap Approach to Eigenvalue Correction
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
IEEE Transactions on Image Processing
A new method of feature fusion and its application in image recognition
Pattern Recognition
Multi-view regression via canonical correlation analysis
COLT'07 Proceedings of the 20th annual conference on Learning theory
Sparse CCA using a Lasso with positivity constraints
Computational Statistics & Data Analysis
SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse canonical correlation analysis
Machine Learning
A Variance Minimization Criterion to Feature Selection Using Laplacian Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A review of speech-based bimodal recognition
IEEE Transactions on Multimedia
Audiovisual Synchronization and Fusion Using Canonical Correlation Analysis
IEEE Transactions on Multimedia
Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data
IEEE Transactions on Image Processing
Inductive multi-task learning with multiple view data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Due to the noise disturbance and limited number of training samples, within-set and between-set sample covariance matrices in canonical correlation analysis (CCA) usually deviate from the true ones. In this paper, we re-estimate within-set and between-set covariance matrices to reduce the negative effect of this deviation. Specifically, we use the idea of fractional order to respectively correct the eigenvalues and singular values in the corresponding sample covariance matrices, and then construct fractional-order within-set and between-set scatter matrices which can obviously alleviate the problem of the deviation. On this basis, a new approach is proposed to reduce the dimensionality of multi-view data for classification tasks, called fractional-order embedding canonical correlation analysis (FECCA). The proposed method is evaluated on various handwritten numeral, face and object recognition problems. Extensive experimental results on the CENPARMI, UCI, AT&T, AR, and COIL-20 databases show that FECCA is very effective and obviously outperforms the existing joint dimensionality reduction or feature extraction methods in terms of classification accuracy. Moreover, its improvements for recognition rates are statistically significant on most cases below the significance level 0.05.