Results 31 to 40 of about 13,849 (192)
A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality [PDF]
, 2017 Symmetric nonnegative matrix factorization (SymNMF) has important applications in data analytics problems such as document clustering, community detection and image segmentation.
Hong, Mingyi +4 more
core +4 more sources
Algorithms for Positive Semidefinite Factorization
, 2017 This paper considers the problem of positive semidefinite factorization (PSD factorization), a generalization of exact nonnegative matrix factorization. Given an $m$-by-$n$ nonnegative matrix $X$ and an integer $k$, the PSD factorization problem consists
Gillis, Nicolas +2 more
core +1 more source
Randomized Algorithms for Symmetric Nonnegative Matrix Factorization
SIAM Journal on Matrix Analysis and ApplicationsSymmetric Nonnegative Matrix Factorization (SymNMF) is a technique in data analysis and machine learning that approximates a symmetric matrix with a product of a nonnegative, low-rank matrix and its transpose. To design faster and more scalable algorithms for SymNMF we develop two randomized algorithms for its computation.
Koby Hayashi +3 more
openaire +3 more sources
Pick matrix conditions for sign-definite solutions of the algebraic Riccati equation [PDF]
, 2001 We study the existence of positive and negative semidefinite solutions of algebraic Riccati equations (ARE) corresponding to linear quadratic problems with an indefinite cost functional.
Rapisarda, Paolo, Trentelman, Harry L.
core +2 more sources
MathematicsRecently, community detection has emerged as a prominent research area in the analysis of complex network structures. Community detection models based on non-negative matrix factorization (NMF) are shallow and fail to fully discover the internal ...
Wei Zhang +4 more
doaj +1 more source
Advanced Intelligent Discovery, EarlyView.Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
, 2014 Olshausen and Field (OF) proposed that neural computations in the primary visual cortex (V1) can be partially modeled by sparse dictionary learning. By minimizing the regularized representation error they derived an online algorithm, which learns Gabor ...
Chklovskii, Dmitri B. +2 more
core +1 more source
Restricted Tweedie stochastic block models
Canadian Journal of Statistics, EarlyView.Abstract The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of nonnegative zero‐inflated continuous edge weights.
Jie Jian, Mu Zhu, Peijun Sang
wiley +1 more source
Nonnegative Tensor Factorization, Completely Positive Tensors and an Hierarchical Elimination Algorithm [PDF]
, 2013 Nonnegative tensor factorization has applications in statistics, computer vision, exploratory multiway data analysis and blind source separation. A symmetric nonnegative tensor, which has a symmetric nonnegative factorization, is called a completely ...
Qi, Liqun, Xu, Changqing, Xu, Yi
core
Polytopes of Minimum Positive Semidefinite Rank
, 2013 The positive semidefinite (psd) rank of a polytope is the smallest $k$ for which the cone of $k \times k$ real symmetric psd matrices admits an affine slice that projects onto the polytope.
Gouveia, João +2 more
core +1 more source