Simulation studies and two real-world examples in biology and finance further illustrate the benefits of the new models.doi:10.1631/jzus.C1200316VIP信息与电子工程前沿(英文)Modeling correlated samples via sparse matrix Gaussian graphical models[J]. Yi-zhou HE,Xi CHEN,Hao WANG.Journal of Zhejiang ...
The sliced coo format for sparse matrix-vector multiplication on CUDA-enabled GPUs Procedia Comput. Sci. (2012) LiuC. et al. Towards efficient spmv on sunway manycore architectures SerraJ. Introduction to mathematical morphology Graphical Models graphical Models and Image Processing computer Vision,...
Motivated by graphical models, we consider the "Sparse Plus Low-rank" decomposition of a positive definite concentration matrix -- the inverse of the covariance matrix. This is a classical problem for which a rich theory and numerical algorithms have been developed. It appears, however, that the...
Graphical models are a powerful tool to estimate a high-dimensional inverse covariance (precision) matrix, which has been applied for portfolio allocation ... TH Lee,E Seregina - 《Papers》 被引量: 0发表: 2020年 Functional brain networks reconstruction using group sparsity-regularized learning Inve...
This matrix models an eight-stage chemical distillation column. Try these commands. nnz(west0479) ans = 1887 formatshortewest0479 west0479 = 479×479 sparse double matrix (1887 nonzeros) (25,1) 1.0000e+00 (31,1) -3.7648e-02 (87,1) -3.4424e-01 ...
javamatrixcollaborative-filteringmatrix-factorizationsystemssparserecommendertensor-factorizationcollaborativetensorfactorizationfilteringrecommender-systemsrecommendation-algorithmsprobabilistic-graphical-models UpdatedJul 13, 2023 Java tensor-compiler/taco Star1.3k
matrixindataanalysistools.Principalcomponentanalysis(PCA),linearand quadraticdiscriminantanalysis(LDAandQDA),inferenceaboutthemeansof thecomponents,andanalysisofindependenceandconditionalindependencein graphicalmodelsallrequireanestimateofthecovariancematrixoritsinverse, ...
We consider joint learning of multiple sparse matrix Gaussian graphical models and propose the joint matrix graphical Lasso to discover the conditional ind... F Huang,S Chen - 《IEEE Transactions on Neural Networks & Learning Systems》 被引量: 11发表: 2015年 Enhanced covariances matrix sparse repr...
sparse matrix-vector multiplication (SpMV); high performance computing (HPC); sparse matrix storage; graphics processing units (GPUs); CSR; ELL; HYB; CSR5; parallelization; heterogeneous computing Graphical Abstract1. Introduction Sparse matrix-vector multiplication (SpMV) is fundamental to many ...
The book includes CSparse, a concise downloadable sparse matrix package that illustrates the algorithms and theorems presented in the book and equips readers with the tools necessary to understand larger and more complex software packages. With a strong emphasis on MATLAB(r) and the C programming ...