IEEE

Enhanced Low-Rank Matrix Decomposition for High-Resolution UAV-SAR Imagery

Abstract: Low-rank matrix decomposition is effective for sparse recovery. However, the conventions are limited in accuracy for high-resolution synthetic aperture radar (SAR) imagery due to the ...
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 ...
Scientific Research Publishing

Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems. Computer, 42, 30-37.

ABSTRACT: The offline course “Home Plant Health Care,” which is available to the senior population, serves as the study object for this paper. Learn how to use artificial intelligence technologies to ...
Frontiers

Geochemical-process extraction and interpretation using matrix factorization: a framework for verifying effectiveness through forward modeling and inversion analysis

Matrix factorization techniques, such as principal component analysis (PCA) and independent component analysis (ICA), are widely used to extract geological processes from geochemical data. However, ...
GitHub

LU Factorization of the G Matrix failed during analyze_power_grid

Hello! I would like to know how to debug this error: "[ERROR PSM-0010] LU factorization of the G Matrix failed. SparseLU solver message: THE MATRIX IS STRUCTURALLY SINGULAR ... ZERO COLUMN AT" I have ...
C&EN

Hierarchical Biclustering of Mouse Pancreas Mass Spectrometry Imaging Data Using Recursive Rank-2 Non-negative Matrix Factorization

STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium ...
Frontiers

Efficient enhancement of low-rank tensor completion via thin QR decomposition

Yunnan Key Laboratory of Statistical Modeling and Data Analysis, Yunnan University, Kunming, China Low-rank tensor completion (LRTC), which aims to complete missing entries from tensors with partially ...
GitHub

fullRankMatrix - Generation of Full Rank Design Matrix

We developed fullRankMatrix primarily for one-hot encoded design matrices used in linear models. In our case, we were faced with a 1-hot encoded design matrix, that had a lot of linearly dependent ...