GitHub

A Python demonstration of speeding up matrix multiplication and reducing memory using a quantum-inspired low-rank SVD trick, with examples in AI.

This repository demonstrates a powerful, classical linear algebra technique—low-rank approximation via Singular Value Decomposition (SVD)—to dramatically accelerate common matrix operations like GEMM ...
IEEE

Semi-Supervised Symmetric Non-Negative Matrix Factorization With Low-Rank Tensor Representation

Abstract: Semi-supervised symmetric non-negative matrix factorization (SNMF) utilizes the available supervisory information (usually in the form of pairwise constraints) to improve the clustering ...
IEEE

Low-Rank Matrix Completion Based on Schattern-p Norm and Gaussian Kernel

Abstract: Low-rank matrix completion has been widely used in many engineering problems, and different matrix norms have been used to approximate the rank function of the matrix to solve the difficulty ...