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Found 5 papers, 3 papers with code
Solving weakly supervised regression problem using low-rank manifold regularization
The utilization of these techniques allows us to increase the quality and stability of the solution.
Kriging in Tensor Train data format
Combination of low-tensor rank techniques and the Fast Fourier transform (FFT) based methods had turned out to be prominent in accelerating various statistical operations such as Kriging, computing conditional covariance, geostatistical optimal design, and others.
Computation Numerical Analysis Methodology
Semi-Supervised Regression using Cluster Ensemble and Low-Rank Co-Association Matrix Decomposition under Uncertainties
The co-association matrix of the ensemble is calculated on both labeled and unlabeled data; this matrix is used as a similarity matrix in the regularization framework to derive the predicted outputs.
HLIBCov: Parallel Hierarchical Matrix Approximation of Large Covariance Matrices and Likelihoods with Applications in Parameter Identification
The main goal of this article is to introduce the parallel hierarchical matrix library HLIBpro to the statistical community.
Computation Numerical Analysis 62F99, 62P12, 62M30 G.3; G.4; J.2
Likelihood Approximation With Hierarchical Matrices For Large Spatial Datasets
We use available measurements to estimate the unknown parameters (variance, smoothness parameter, and covariance length) of a covariance function by maximizing the joint Gaussian log-likelihood function.
Computation 62F99, 62P12, 62M30 G.3; G.4; J.2
