Frontiers

Low-Rank Plus Sparse Decomposition of fMRI Data With Application to Alzheimer's Disease

Figure 1. Flowchart for the proposed robust functional brain connectivity pipeline. The rest of the paper is organized as follows. In Section 2, we introduce some technical details on low rank plus ...
IEEE

Simultaneously Structured Models With Application to Sparse and Low-Rank Matrices

Abstract: Recovering structured models (e.g., sparse or group-sparse vectors, low-rank matrices) given a few linear observations have been well-studied recently. In various applications in signal ...
GitHub

Low Rank plus Sparse Decomposition of Covariance Matrices using Neural Network Parametrization

This repository is the official implementation of the paper Low Rank plus Sparse Decomposition of Covariance Matrices using Neural Network Parametrization. Generation of a low rank and of a sparse ...
GitHub

Low-Rank Approximation of Matrices for PMI-based Word Embeddings

Alena Sorokina, Aidana Karipbayeva, Zhenisbek Assylbekov. International Conference on Computational Linguistics and Intelligent Text Processing, 2019. Piblished in Lecture Notes in Computer Science ...
Scientific Research Publishing

Low-Rank Positive Approximants of Symmetric Matrices ()

are the eigenvalues of in decreasing order. If, then and the spectral decomposition (1.2) coincides with the SVD of. In this case the solution of (1.1) is given by the Eckart-Young-Mirsky theorem. See ...
Microsoft

Efficient Matrix Sensing Using Rank-1 Gaussian Measurements

In this paper, we study the problem of low-rank matrix sensing where the goal is to reconstruct a matrix {\em exactly} using a small number of linear measurements. Existing methods for the problem ...
IEEE

Recovering Low-Rank Matrices From Few Coefficients in Any Basis

Abstract: We present novel techniques for analyzing the problem of low-rank matrix recovery. The methods are both considerably simpler and more general than previous approaches. It is shown that an ...
JSTOR Daily

On Rank Estimation in Symmetric Matrices: The Case of Indefinite Matrix Estimators

This is a preview. Log in through your library . Abstract In this paper we consider estimating the rank of an unknown symmetric matrix based on a symmetric, asymptotically normal estimator of the ...