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Randomized Rank-Revealing QLP for Low-Rank Matrix Decomposition [PDF]
IEEE Access, 2023 The pivoted QLP decomposition is computed through two consecutive pivoted QR decompositions. It is an approximation to the computationally prohibitive singular value decomposition (SVD). This work is concerned with a partial QLP decomposition of matrices
Maboud F. Kaloorazi +4 more
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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
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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
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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
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Prime decomposition of quadratic matrix polynomials
AIMS Mathematics, 2021 We study the prime decomposition of a quadratic monic matrix polynomial. From the prime decomposition of a quadratic matrix polynomial, we obtain a formula of the general solution to the corresponding second-order differential equation.
Yunbo Tian, Sheng Chen
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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
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Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions [PDF]
, 2011 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, N. +2 more
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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
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On decomposition of k-tridiagonal ℓ-Toeplitz matrices and its applications
Special Matrices, 2015 We consider a k-tridiagonal ℓ-Toeplitz matrix as one of generalizations of a tridiagonal Toeplitz matrix. In the present paper, we provide a decomposition of the matrix under a certain condition.
Ohashi A., Sogabe T., Usuda T.S.
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Rank-Sparsity Incoherence for Matrix Decomposition [PDF]
, 2009 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.
Chandrasekaran, Venkat +3 more
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