Principal Component Analysis
Advanced textbooks in control and signal processingPublished 1 January 2001
Leo H. Chiang, Evan L. Russell, Richard D. Braatz
Citations6,631
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Abstract
By projecting the data into a lower-dimensional space that accurately characterizes the state of the process, dimensionality reduction techniques can greatly simplify and improve process monitoring procedures. Principal component analysis (PCA) is such a dimensionality reduction technique. It produces a lower-dimensional representation in a way that preserves the correlation structure between the process variables, and is optimal in terms of capturing the variability in the data.
Keywords
Engineering