Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Orthogonal Moment Features for Use With Parametric and Non-Parametric Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Recognition of Handwritten Numerals Using Gabor Features
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Combining Pattern Classifiers: Methods and Algorithms
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Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimodal speaker/speech recognition using lip motion, lip texture and audio
Signal Processing - Special section: Multimodal human-computer interfaces
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tensor Approximation Approach to Dimensionality Reduction
International Journal of Computer Vision
Multiple feature fusion by subspace learning
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Active learning with multiple views
Journal of Artificial Intelligence Research
IEEE Transactions on Neural Networks
Multiple view semi-supervised dimensionality reduction
Pattern Recognition
On the equivalence between canonical correlation analysis and orthonormalized partial least squares
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Uncorrelated multilinear principal component analysis for unsupervised multilinear subspace learning
IEEE Transactions on Neural Networks
Letters: Fusion of classifiers for protein fold recognition
Neurocomputing
Improving face recognition by combination of natural and Gabor faces
Pattern Recognition Letters
Feature fusion using locally linear embedding for classification
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Multilinear Discriminant Analysis for Face Recognition
IEEE Transactions on Image Processing
Tensor Discriminant Color Space for Face Recognition
IEEE Transactions on Image Processing
Color face tensor factorization and slicing for illumination-robust recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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Canonical correlation analysis (CCA) and partial least squares (PLS) are always used as fusing two feature sets. How to extend them to fuse multiple features in a generalized way is still an unsolved problem. In this paper, we propose a novel feature fusion method called multiple component analysis (MCA). By constructing a higher-order tensor, all kinds of information are fused into the covariance tensor. Then orthogonal subspaces corresponding to each feature set are learned through tensor singular value decomposition (SVD), that couples dimension reduction and feature fusion together. Compared with multiple feature fusion by subspace learning (MFFSL), our method has the ability to represent fused data more efficiently and discriminatively in very few components. And it is shown that principle component analysis (PCA) and PLS are special cases of our method when there are only one set and two sets of features respectively. Extensive experiments on both handwritten numerals classification and face recognition demonstrate the effectiveness and robustness of the proposed method.