A random version of principal component analysis in data clustering
arXiv:1610.08664 · doi:10.1016/j.compbiolchem.2018.01.009
Abstract
Principal component analysis (PCA) is a widespread technique for data analysis that relies on the covariance-correlation matrix of the analyzed data. However to properly work with high-dimensional data, PCA poses severe mathematical constraints on the minimum number of different replicates or samples that must be included in the analysis. Here we show that a modified algorithm works not only on well dimensioned datasets, but also on degenerated ones.
arXiv comment18 pages, 6 figures, 2 tables
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