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The novel aspect is the combination of the general technical principle of projection pursuit for multivariate data with the neutron multiplication eigenvalue problem in the nuclear engineering discipline.
We first develop a strategy for multiple imputations for repeated measures data under a cell-means model that is applicable for any multivariate data with small samples.
We note that the tools we develop meet analogous purposes in all data-driven fields, as the fundamental problem of classification of multivariate data with complex geometric dependencies is field-spanning.
A framework of using t mixture models with fourteen eigen-decomposed covariance structures for the unsupervised learning of heterogeneous multivariate data with possible missing values is designed and implemented.
There is a need for tractable models for multivariate data with nonstandard dependence structures.
To get a reliable analysis of multivariate data with outliers, robust estimators are required that can resist possible outliers.
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Sarkar, D. Lattice: multivariate data visualization with R. (Springer Science & Business Media, 2008).
Furthermore, by referring to the topological types of the singular fibers embedded in the data domain, we can effectively extract meaningful features from multivariate data samples with minimal cost.
As Section 4.3 reveals, regularization brings about benefits for multivariate data also with a small number of variables.
To represent multivariate data associated with a node, Cytoscape can control the visualization of each node using a custom graphical image (since version 2.3) through a programming API.
We formulated a multivariate data matrix with n (rows) as the sample of enhancers and p (columns) the number of TFs for training and control data sets (for control data set see Additional file 6: Table S5).
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