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IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
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SIAM Journal on Matrix Analysis and Applications
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
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Beyond streams and graphs: dynamic tensor analysis
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Feature Reduction via Generalized Uncorrelated Linear Discriminant Analysis
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ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
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General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
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Statistical Analysis and Data Mining
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A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
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Selecting discriminant eigenfaces for face recognition
Pattern Recognition Letters
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AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Robust linear dimensionality reduction
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Multilinear Discriminant Analysis for Face Recognition
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Reconstruction and Recognition of Tensor-Based Objects With Concurrent Subspaces Analysis
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Visual recognition of continuous hand postures
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A neural-network appearance-based 3-D object recognition using independent component analysis
IEEE Transactions on Neural Networks
MPCA: Multilinear Principal Component Analysis of Tensor Objects
IEEE Transactions on Neural Networks
Tensor distance based multilinear locality-preserved maximum information embedding
IEEE Transactions on Neural Networks
A survey of multilinear subspace learning for tensor data
Pattern Recognition
Feature Fusion Using Multiple Component Analysis
Neural Processing Letters
Gait identification based on MPCA reduction of a video recordings data
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
UMPCA based feature extraction for ECG
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Learning canonical correlations of paired tensor sets via tensor-to-vector projection
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Multi-linear neighborhood preserving projection for face recognition
Pattern Recognition
Sparse tensor embedding based multispectral face recognition
Neurocomputing
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This paper proposes an uncorrelated multilinear principal component analysis (UMPCA) algorithm for unsupervised subspace learning of tensorial data. It should be viewed as a multilinear extension of the classical principal component analysis (PCA) framework. Through successive variance maximization, UMPCA seeks a tensor-to-vector projection (TVP) that captures most of the variation in the original tensorial input while producing uncorrelated features. The solution consists of sequential iterative steps based on the alternating projection method. In addition to deriving the UMPCA framework, this work offers a way to systematically determine the maximum number of uncorrelated multilinear features that can be extracted by the method. UMPCA is compared against the baseline PCA solution and its five state-of-the-art multilinear extensions, namely two-dimensional PCA (2DPCA), concurrent subspaces analysis (CSA), tensor rank-one decomposition (TROD), generalized PCA (GPCA), and multilinear PCA (MPCA), on the tasks of unsupervised face and gait recognition. Experimental results included in this paper suggest that UMPCA is particularly effective in determining the low-dimensional projection space needed in such recognition tasks.