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Decomposition of a symmetric matrix
Numerische Mathematik, 1976An algorithm is presented to compute a triangular factorization and the inertia of a symmetric matrix. The algorithm is stable even when the matrix is not positive definite and is as fast as Cholesky. Programs for solving associated systems of linear equations are included.
J. Bunch, L. Kaufman, B. Parlett
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Householder's tridiagonalization of a symmetric matrix
Numerische Mathematik, 1968In an early paper in this series [4] Householder’s algorithm for the tridiagonalization of a real symmetric matrix was discussed. In the light of experience gained since its publication and in view of its importance it seems worthwhile to issue improved versions of the procedure given there.
R. Martin, C. Reinsch, J. H. Wilkinson
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IEEE Transactions on Neural Networks and Learning Systems, 2021
Community detection is a popular yet thorny issue in social network analysis. A symmetric and nonnegative matrix factorization (SNMF) model based on a nonnegative multiplicative update (NMU) scheme is frequently adopted to address it.
Xin Luo+4 more
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Community detection is a popular yet thorny issue in social network analysis. A symmetric and nonnegative matrix factorization (SNMF) model based on a nonnegative multiplicative update (NMU) scheme is frequently adopted to address it.
Xin Luo+4 more
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Semisupervised Adaptive Symmetric Non-Negative Matrix Factorization
IEEE Transactions on Cybernetics, 2020As a variant of non-negative matrix factorization (NMF), symmetric NMF (SymNMF) can generate the clustering result without additional post-processing, by decomposing a similarity matrix into the product of a clustering indicator matrix and its transpose.
Yuheng Jia+3 more
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Approximating a Symmetric Matrix [PDF]
We examine the least squares approximation C to a symmetric matrix B, when all diagonal elements get weight w relative to all nondiagonal elements. When B has positivity p and C is constrained to be positive semi-definite, our main result states that, when w ≥1/2, then the rank of C is never greater than p, and when w ≤1/2 then the rank of C is at ...
R. A. Bailey, John C. Gower
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Fundamental limits of symmetric low-rank matrix estimation
Probability theory and related fields, 2016We consider the high-dimensional inference problem where the signal is a low-rank symmetric matrix which is corrupted by an additive Gaussian noise. Given a probabilistic model for the low-rank matrix, we compute the limit in the large dimension setting ...
M. Lelarge, Léo Miolane
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Pairwise Constraint Propagation-Induced Symmetric Nonnegative Matrix Factorization
IEEE Transactions on Neural Networks and Learning Systems, 2018As a variant of nonnegative matrix factorization (NMF), symmetric NMF (SNMF) has shown to be effective for capturing the cluster structure embedded in the graph representation.
Wenhui Wu+3 more
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