GitHub

Implement Gaussian Filtering Using Sparse Matrix Instead of Built-in Functions

Currently, I need to perform Gaussian filtering on images, but I want to avoid using MATLAB's built-in functions. Given an image of size N x N, my idea is to construct a sparse matrix where both ...
Comicbook.com

One of the Most Confusing Things About The Matrix Sequels Actually Has an Answer

Click to share on X (Opens in new window) X Click to share on Facebook (Opens in new window) Facebook Michael ends up finding himself trapped on the roof of his school with the Agents closing in on ...
Frontiers

Based on the N2N-SAMP for sparse underwater acoustic channel estimation

1 Ocean Engineering Research Center, Hangzhou Dianzi University, Hangzhou, China 2 School of Mecharical Engineering, Hangzhou Dianzi University, Hangzhou, China Introduction: Orthogonal Frequency ...
IEEE

Efficient Characteristic Mode Analysis Based on Data-Sparse Matrix Algorithm

Abstract: The implicitly restarted Arnoldi method (IRAM), which relies on Krylov subspace iteration, is an effective approach for extracting desired partial eigenpairs in characteristic mode analysis ...
acm.org

Solving Sparse Linear Systems Faster than Matrix Multiplication

Presenting an algorithm that solves linear systems with sparse coefficient matrices asymptotically faster than matrix multiplication for any ω > 2. Our algorithm can be viewed as an efficient, ...
IEEE

Learning Sparse High-Dimensional Matrix-Valued Graphical Models From Dependent Data

Abstract: We consider the problem of inferring the conditional independence graph (CIG) of a sparse, high-dimensional, stationary matrix-variate Gaussian time series. All past work on high-dimensional ...
Scientific Research Publishing

Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors ()

Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust ...