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• W4210544666 abstract "The tensor singular value decomposition (t-SVD) based on an algebra of circulants is an effective multilinear sub- space learning technique for dimensionality reduction and data classification. Unfortunately, the computational cost associated with computing the t-SVD can become prohibitively expensive, particularly when dealing with very large data sets. In this paper, we present a computationally efficient approach for estimating the t-SVD by capitalizing on the correlations of the data in the temporal dimension. The approach proceeds by extending our prior work on fast eigenspace decompositions by transforming the tensor data from the spatial domain to the spectral domain in order to obtain reduced order harmonic tensor. The t-SVD can then be applied in the transform domain thereby significantly reducing the computational burden. Experimental results which are presented on the extended Yale-B, COIL-100, and MNIST data sets show the proposed method provides considerable computational savings with the approximated subspaces that are nearly the same as the true subspaces as computed via the t-SVD." @default.
• W4210544666 created "2022-02-08" @default.
• W4210544666 creator A5067467232 @default.
• W4210544666 creator A5080469720 @default.
• W4210544666 creator A5091589893 @default.
• W4210544666 date "2021-12-01" @default.
• W4210544666 modified "2023-09-26" @default.
• W4210544666 title "Fast Tensor Singular Value Decomposition Using the Low-Resolution Features of Tensors" @default.
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• W4210544666 doi "https://doi.org/10.1109/icmla52953.2021.00088" @default.
• W4210544666 hasPublicationYear "2021" @default.
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