Linear Discriminant Analysis (LDA) and Multilinear Principal Component Analysis (MPCA) are leading subspace methods for achieving dimension reduction based on supervised learning. Both LDA and MPCA use class labels of data samples to calculate subspaces
Park SungWon, Savvides Marios
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Face recognition system is gaining more importance in social networks and surveillance. The face recognition task is complex due to the variations in illumination, expression, occlusion, aging and pose.
A. Thamizharasi, J.S. Jayasudha
doaj
Enhancing unsupervised bearing fault diagnosis through structured prediction in latent subspace. [PDF]
Liu C, Hu R, Fang X, Luo W, Zhu C.
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Rolling Bearing Fault Diagnosis Based on Multi-Source Domain Joint Structure Preservation Transfer with Autoencoder. [PDF]
Jiang Q +7 more
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Convolutional neural network models describe the encoding subspace of local circuits in auditory cortex. [PDF]
Wingert JC +3 more
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Overlap-Kernel EPI: Estimating MRI Shot-to-Shot Phase Variations by Shifted-Kernel Extraction From Overlap Regions at Arbitrary k-Space Locations. [PDF]
Tian R +3 more
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Covariate-invariant gait recognition using random subspace method and its extensions [PDF]
Compared with other biometric traits like fingerprint or iris, the most significant advantage of gait is that it can be used for remote human identification without cooperation from the subjects. The technology of gait recognition may play an important role in crime prevention, law enforcement, etc. Yet the performance of automatic gait recognition may
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Independent Encoding of Orientation and Mean Luminance by Mouse Visual Cortex. [PDF]
O'Shea RT, Wei XX, Priebe NJ.
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A Novel, Variance Component-Based Method for Detecting Brain-Behavior Associations in Neuroimaging Data. [PDF]
Chen C +5 more
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Asymmetric canonical correlation analysis of Riemannian and high-dimensional data. [PDF]
Buenfil J, Lila E.
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