Sparse principal component analysis (SPCA) extends classical principal component analysis to settings where the number of variables greatly exceeds the number of observations. By imposing sparsity ...
High-dimensional datasets arise across disciplines from genomics and neuroimaging to finance and social science. As the number of variables grows, statistical inference and predictive modelling become ...
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