Abstract: This paper focuses on optimizing the Matrix-Power Kernel (MPK), which relies on a series of Sparse Matrix-Vector multiplications (SpMVs) using the same sparse matrix. MPK is a crucial ...
Abstract: We consider matrix iterative subspace filters for solving minimum mean-squared error estimation problems in low-dimensional subspaces. Very general ...
In this paper, we study the matrix denoising model Y = S + X, where S is a low rank deterministic signal matrix and X is a random noise matrix, and both are M × n. In the scenario that M and n are ...
Some algorithms based upon a projection process onto the Krylov subspace $K_m = \operatorname{Span}(r_0, Ar_0, \ldots, A^{m - 1}r_0)$ are developed, generalizing the ...
This code corresponding to the paper: Latent Space Factorisation and Manipulation via Matrix Subspace Projection (ICML2020). The main website is here https://xiao.ac/proj/msp. To train and test the ...
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