In this paper, the GTM is redefined as a constrained mixture of t -distributions: the t -GTM, and the Expectation–Maximization algorithm that is used to fit the ...
The resulting GTM model plays a double role: it deals robustly with outliers while it simultaneously imputes missing values, allowing the exploration of ...
In this paper, the GTM is redefined as a constrained mixture of t-distributions: the t-GTM, and the Expectation-Maximization algorithm that is used to fit the ...
This paper defines an alternative variational formulation of GTM that provides a full Bayesian treatment to a Gaussian process (GP)-based variation of the ...
TL;DR: This paper redefined the Generative Topographic Mapping as a constrained mixture of t-distributions: the t-GTM, and the Expectation-Maximization ...
In this report, the GTM is redefined as a constrained mixture of t-distributions: the t-GTM, and the Expectation-Maximization algorithm that is used to fit the ...
The Generative Topographic Mapping (GTM) was originally conceived as a probabilistic alternative to the well-known, neural network-inspired, Self-Organizing ...
This report redefined the Generative Topographic Mapping as a constrained mixture of t-distributions: the t-GTM, and the Expectation-Maximization algorithm ...
Studies on handling missing data in the context of GMMs recommend using full-information maximum likelihood (FI) over single-stage multiple imputation (MI; ...
Títol: Missing data imputation through GTM as a mixture of t-distributions. Autors: Vellido, A.,. Investigadors/es (PRC):, Vellido, Alfredo.
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