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Showing 1 to 2 of 2 for “"Sparse factor analysis"”.
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Bayesian and Information-Theoretic Learning of High Dimensional Data
<p>The concept of sparseness is harnessed to learn a low dimensional representation of high dimensional data. This sparseness assumption is exploited in multiple ways. In the Bayesian Elastic Net, a small number of correlated features are identified for the response variable. In the sparse Factor …
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Dynamic factor analysis with dependent Gaussian processes for high-dimensional biomarker trajectories
… pathway expression trajectories via Bayesian sparse factor analysis in Chapter 2. Our proposal is the first attempt to relax the classical assumption of independent factors for longitudinal data and has demonstrated a superior performance in recovering the shape of pathway expression …