by SPCA, SDA and the tensor-based GTDA algorithms, we proposed Sparse Tensor Discriminant Analysis (STDA) for feature extraction and classification. Our ...
In this paper, a novel discriminant subspace learning method called sparse tensor discriminant analysis (STDA) is proposed, which further extends the ...
Here we propose a Sparse Semisupervised Sparse Multilinear Discriminant Analysis (SSSMDA) for electrocardiograms (ECGs), our method consider the distirbution of ...
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(2018) is a recent review on tensor sparse ... 2006) have shown promising perfor- mances of matrix and tensor discriminant analysis, where the key idea is based.
Aug 30, 2013 · In this paper, a novel discriminant subspace learning method called sparse tensor discriminant analysis (STDA) is pro- posed, which further ...
Oct 1, 2013 · In this paper, a novel discriminant subspace learning method called sparse tensor discriminant analysis (STDA) is proposed, which further ...
In this paper, we propose a novel sparse subspace learning method named discriminant sparse tensor neighborhood preserving embedding (DSTNPE) which incorporates ...
More recently, likelihood-based matrix/tensor discriminant analysis models and ... (2017), 'Store: sparse tensor response regression and neuroimaging analysis', ...
Sep 9, 2011 · We propose sparse discriminant analysis, a method for performing linear discriminant analysis with a sparseness criterion imposed such that ...
Missing: Tensor | Show results with:Tensor
Sparse discriminant analysis is based on the optimal scoring inter of linear discriminant analysis, and can be extended to perform sparse discrimination via ...
Missing: Tensor | Show results with:Tensor