Results 11 to 20 of about 1,040,614 (343)

The rank of sparse random matrices [PDF]

Random Structures & Algorithms, 2020
AbstractWe determine the asymptotic normalized rank of a random matrix over an arbitrary field with prescribed numbers of nonzero entries in each row and column. As an application we obtain a formula for the rate of low‐density parity check codes. This formula vindicates a conjecture of Lelarge (2013).
Amin Coja-Oghlan, Alperen Ali Ergür, Pu Gao, Samuel Hetterich, Maurice Rolvien   +4 more
openaire   +6 more sources

Practical sparse matrices in C++ with hybrid storage and template-based expression optimisation [PDF]

Mathematical and Computational Applications, 2019
Despite the importance of sparse matrices in numerous fields of science, software implementations remain difficult to use for non-expert users, generally requiring the understanding of the underlying details of the chosen sparse matrix storage format. In
Sanderson, Conrad, Curtin, Ryan, Conrad Sanderson, Ryan Curtin   +3 more
core   +3 more sources

Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
It is known that the decomposition in low-rank and sparse matrices (L+S for short) can be achieved by several Robust PCA techniques. Besides the low rankness, the local smoothness (LSS) is a vitally essential prior for many real-world matrix data such as
Jiangjun Peng, Yao Wang, Hongying Zhang, Jianjun Wang, Deyu Meng   +4 more
semanticscholar   +1 more source

Subset Selection in Sparse Matrices [PDF]

SIAM Journal on Optimization, 2020
In subset selection we search for the best linear predictor that involves a small subset of variables. From a computational complexity viewpoint, subset selection is NP-hard and few classes are known to be solvable in polynomial time. Using mainly tools from discrete geometry, we show that some sparsity conditions on the original data matrix allow us ...
Alberto Del Pia, Santanu S. Dey, Robert Weismantel   +2 more
openaire   +3 more sources

Fast and Accurate Non-Negative Latent Factor Analysis of High-Dimensional and Sparse Matrices in Recommender Systems

IEEE Transactions on Knowledge and Data Engineering, 2023
A fast non-negative latent factor (FNLF) model for a high-dimensional and sparse (HiDS) matrix adopts a Single Latent Factor-dependent, Non-negative, Multiplicative and Momentum-incorporated Update (SLF-NM2U) algorithm, which enables its fast convergence.
Xin Luo, Yue Zhou, Zhigang Liu, Mengchu Zhou   +3 more
semanticscholar   +1 more source

cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices

bioRxiv, 2021
Quickly and flexibly exploring high-dimensional datasets, such as scRNAseq data, is underserved but critical for hypothesis generation, dataset annotation, publication, sharing, and community reuse.
Colin Megill, Bruce Martin, Charlotte Weaver, Sidney M. Bell, Lia Prins, Seve Badajoz, B. McCandless, A. Pisco, Marcus Kinsella, Fiona Griffin, Justin Kiggins, Genevieve E. Haliburton, Arathi Mani, Matthew Weiden, Madison I. Dunitz, Maximilian Lombardo, Timmy Huang, Trent Smith, Signe Chambers, J. Freeman, J. Cool, Ambrose J. Carr   +21 more
semanticscholar   +1 more source

Distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices [PDF]

International Conference for High Performance Computing, Networking, Storage and Analysis, 2020
Identifying similar protein sequences is a core step in many computational biology pipelines such as detection of homologous protein sequences, generation of similarity protein graphs for downstream analysis, functional annotation, and gene location ...
Oguz Selvitopi, S. Ekanayake, Giulia Guidi, Georgios A. Pavlopoulos, A. Azad, A. Buluç   +5 more
semanticscholar   +1 more source

Sparse random block matrices [PDF]

Journal of Physics A: Mathematical and Theoretical, 2022
Abstract The spectral moments of ensembles of sparse random block matrices are analytically evaluated in the limit of large order. The structure of the sparse matrix corresponds to the Erdös–Renyi random graph. The blocks are i.i.d. random matrices of the classical ensembles GOE or GUE. The moments are evaluated for finite or infinite
Giovanni M Cicuta, Mario Pernici
openaire   +3 more sources

Sparse block-structured random matrices: universality

Journal of Physics: Complexity, 2023
We study ensembles of sparse block-structured random matrices generated from the adjacency matrix of a Erdös–Renyi random graph with N vertices of average degree Z , inserting a real symmetric d  ×  d random block at each non-vanishing entry. We consider
Giovanni M Cicuta, Mario Pernici
doaj   +1 more source

Robust sparse recovery with sparse Bernoulli matrices via expanders

Applied and Computational Harmonic Analysis
Sparse binary matrices are of great interest in the field of sparse recovery, nonnegative compressed sensing, statistics in networks, and theoretical computer science. This class of matrices makes it possible to perform signal recovery with lower storage
Abdalla, Pedro
exaly   +1 more source