Sankhyā: The Indian Journal of Statistics, Series A (1961-2002), Vol. 26, No. 4 (Dec., 1964), pp. 329-358 (30 pages) The paper provides various interpretations of principal components in the analysis ...
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 ...
Transforming a dataset into one with fewer columns is more complicated than it might seem, explains Dr. James McCaffrey of Microsoft Research in this full-code, step-by-step machine learning tutorial.
A very important technique in unsupervised machine learning as well as dimensionality reduction is Principal Component Analysis (PCA). But PCA is difficult to understand without the fundamental ...
Results are presented from Principal Components Analysis of three data matrices (pollen concentration per c.c., percentages of total pollen, and analyses for eight or nine chemical elements) of c.
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