A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Meta-clustering of gene expression data and literature-based information
ACM SIGKDD Explorations Newsletter
Algorithm 862: MATLAB tensor classes for fast algorithm prototyping
ACM Transactions on Mathematical Software (TOMS)
Multiple feature fusion by subspace learning
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Computational Statistics & Data Analysis
Joint blind source separation by multiset canonical correlation analysis
IEEE Transactions on Signal Processing
A Bootstrap Approach to Eigenvalue Correction
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
A new method of feature fusion and its application in image recognition
Pattern Recognition
Regularized Discriminant Analysis, Ridge Regression and Beyond
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Fusion Using Multiple Component Analysis
Neural Processing Letters
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
Group Study of Simulated Driving fMRI Data by Multiset Canonical Correlation Analysis
Journal of Signal Processing Systems
A shape- and texture-based enhanced Fisher classifier for face recognition
IEEE Transactions on Image Processing
Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data
IEEE Transactions on Image Processing
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The sample covariance matrices in multiset canonical correlation analysis (MCCA) usually deviate from the true ones owing to noise and the limited number of training samples. In this paper, we thus re-estimate the covariance matrices by using the idea of fractional order embedding to respectively correct sample eigenvalues and singular values. Then, we define fractional-order within-set and between-set scatter matrices, which can significantly reduce the deviation of sample covariance matrices. At last, a novel multiset canonical correlation method is presented for multiset feature fusion, called fractional-order embedding multiset canonical correlations (FEMCCs). The proposed FEMCC method first performs joint feature extraction on multiple sets of feature vectors that are obtained from the same objects, and then fuse the extracted correlation features by a given fusion strategy to form discriminative feature vectors for classification tasks. The proposed method is applied to face recognition and object category classification and is examined using the AR, AT&T, and CMU PIE face image databases and the ETH-80 object database. Numerous experimental results demonstrate the effectiveness and robustness of the FEMCC fusion method.