High-dimensional data arise when the number of measured variables far exceeds the number of observations, a situation common in genomics, image analysis and finance. This imbalance introduces ...
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 ...
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