Sparse Principal Component Analysis (sparse PCA) represents a significant advance in the field of dimensionality reduction for high-dimensional data. Unlike conventional Principal Component Analysis ...
The Annals of Statistics, Vol. 43, No. 3 (June 2015), pp. 1300-1322 (23 pages) Estimating the leading principal components of data, assuming they are sparse, is a central task in modern ...
We propose a semiparametric method for conducting scale-invariant sparse principal component analysis (PCA) on high-dimensional non-Gaussian data. Compared with sparse PCA, our method has a weaker ...
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