Portal de Periódicos da CAPES

Principal Component Analysis

2001; Linguagem: Inglês

10.1007/978-1-4471-0347-9_4

1439-2232

Leo H. Chiang, Evan L. Russell, Richard D. Braatz,

Fault Detection and Control Systems

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.