The SC-RCD algorithm constrains the dynamics of (block) randomized coordinate descent – a simple, lightweight iterative solver – within a particular subspace corresponding to an efficiently computable ...
Abstract: Sparse subspace clustering algorithm (SSC) is one of the efficient methods for hyperspectral imagery (HSI) segmentation. However, it does not consider the spectral information and space ...
Abstract: We consider matrix iterative subspace filters for solving minimum mean-squared error estimation problems in low-dimensional subspaces. Very general ...
Low-rank self-representation based subspace learning has confirmed its great effectiveness in a broad range of applications. Nevertheless, existing studies mainly focus on exploring the global linear ...
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 ...
The Hermitian pair (A,B) is called definite if some real linear combination of the matrices A and B is a positive definite matrix. There are several reasons why it is important to detect whether a ...
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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