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Tensor rank estimation and completion via CP-based nuclear norm
Tensor completion (TC) is a challenging problem of recovering missing entries of a tensor from its partial observation. One main TC …
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Bilinear probabilistic canonical correlation analysis via hybrid concatenations
Canonical Correlation Analysis (CCA) is a classical technique for two-view correlation analysis, while Probabilistic CCA (PCCA) …
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Multilinear regression for embedded feature selection with application to fMRI analysis
Embedded feature selection is effective when both prediction and interpretation are needed. The Lasso and its extensions are standard …
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EcoICA: Skewness-based ICA via eigenvectors of cumulant operator
Independent component analysis (ICA) is an important unsupervised learning method. Most popular ICA methods use kurtosis as a metric of …
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Proper inner product with mean displacement for gaussian noise invariant ICA
Independent Component Analysis (ICA) is a classical method for Blind Source Separation (BSS). In this paper, we are interested in ICA …
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Learning compact binary codes from higher-order tensors via free-form reshaping and binarized multilinear PCA
For big, high-dimensional dense features, it is important to learn compact binary codes or compress them for greater memory efficiency. …
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Probabilistic rank-one matrix analysis with concurrent regularization
As a classical subspace learning method, Probabilistic PCA (PPCA) has been extended to several bilinear variants for dealing with …
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Learning tensor-based features for whole-brain fMRI classification
This paper presents a novel tensor-based feature learning approach for whole-brain fMRI classification. Whole-brain fMRI data have high …
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Semi-orthogonal multilinear PCA with relaxed start
Principal component analysis (PCA) is an unsupervised method for learning low-dimensional features with orthogonal projections. …
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Learning modewise independent components from tensor data using multilinear mixing model
Independent component analysis (ICA) is a popular unsupervised learning method. This paper extends it to multilinear modewise ICA …
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