Results 1 to 10 of about 834,101 (163)
Rank-Sparsity Incoherence for Matrix Decomposition [PDF]
, 2011 Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into its sparse and low-rank components.
Venkat Chandrasekaran +3 more
openalex +10 more sources
Mathematical Modelling and Control, 2023 This paper investigated the Kronecker product (KP) decomposition of the Boolean matrix and analyzed the topological structure of Kronecker product Boolean networks (KPBNs).
Xiaomeng Wei, Haitao Li, Guodong Zhao
doaj +1 more source
Singular Value Decomposition of Spatial Matrices
Современные информационные технологии и IT-образование, 2022 Singular value decomposition is a basic building block which is used in solution of many different problems. In cases when dimensionality of a problem exceeds two, a generalization of a singular value decomposition – tensor decompositions – are used ...
Pavel Iljin, Tatiana Samoilova
doaj +1 more source
Energy-Based Adaptive CUR Matrix Decomposition
IEEE Access, 2023 CUR decompositions are interpretable data analysis tools that express a data matrix in terms of a small number of actual columns and/or actual rows of the data matrix.
Liwen Xu, Xuejiao Zhao, Yongxia Zhang
doaj +1 more source
IET Control Theory & Applications, 2023 The approximating sequence Riccati equation method is an efficient approach for solving the nonlinear optimal control problems, but its neglect of nonlinear dynamics and necessary optimality condition makes the control law difficult to satisfy the ...
Jianfeng Sun, Xuesong Chen
doaj +1 more source
Frontiers in Neuroscience, 2022 Multi-modal magnetic resonance imaging (MRI) is widely used for diagnosing brain disease in clinical practice. However, the high-dimensionality of MRI images is challenging when training a convolution neural network.
Liangliang Liu +5 more
doaj +1 more source
Quantum Fourier transform revisited [PDF]
, 2020 The fast Fourier transform (FFT) is one of the most successful numerical algorithms of the 20th century and has found numerous applications in many branches of computational science and engineering.
Bullock SS +5 more
core +2 more sources
Multiresolution matrix factorisation as a compression method for smart meter data
The Journal of Engineering, 2020 The development of a smart grid electricity distribution network with advanced technology in smart metering will produce a massive amount of data. However, the limitation in communication network bandwidth makes it hard to transmit these data to the ...
Arfah Ahmad +5 more
doaj +1 more source
Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions [PDF]
, 2010 Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-revealing QR decomposition, play a central role in data analysis and scientific computing.
Halko, Nathan +2 more
core +6 more sources
Incremental multi‐view correlated feature learning based on non‐negative matrix factorisation
IET Computer Vision, 2021 In real‐world applications, large amounts of data from multiple sources come in the form of streams. This makes multi‐view feature learning cost much time when new instances rise incrementally.
Liang Zhao +3 more
doaj +1 more source