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Multilinear subspace learning

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Multilinear subspace learning

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Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition

PCA and deep learning based myoelectric grasping control of a prosthetic hand

Multigrid Methods Combined With Low-Rank Approximation For Tensor-Structured Markov Chains

Subspace Regularized Dynamic Time Warping for Spoken Query Detection

Scalable Low-rank Matrix and Tensor Decomposition on Graphs

Fourier dimensionality reduction of radio-interferometric data

How close are the eigenvectors and eigenvalues of the sample and actual covariance matrices?

Reduced Basis Methods: From Low-Rank Matrices To Low-Rank Tensors

Subspace Detection of DNN Posterior Probabilities via Sparse Representation for Query by Example Spoken Term Detection

On low-rank approximability of solutions to high-dimensional operator equations and eigenvalue problems