Definition
PCA is a statistical tool used to explore complex series of multivariate observations by which we can summarize a great amount of data through recognition of its most relevant information content. PCA behaves as a filtering-compression technique that captures the main trends in the data while revealing their underlying structure (Johnson and Wichner 1998; Brereton 2003; Wall et al. 2003).
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Motivation Problem
When in a set of n “objects” (the experimental units) m attributes are measured, a cloud of points would appear when objects are represented in a space where each axis corresponds to one variable. PCA focuses on the “shape” of such cloud, trying to capture the cloud’s dominating directions(the eigenvectors). When these data are projected onto a lower-dimensional subspace, delimited by the eigenvectors of the cloud, a clearer visualization of the relationship between...
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References
Brereton R (2003) Data analysis for the laboratory to chemistry plant. Wiley, New York, pp 121–223 (Chap 4)
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Guebel, D.V., Torres, N.V. (2013). Principal Component Analysis (PCA). In: Dubitzky, W., Wolkenhauer, O., Cho, KH., Yokota, H. (eds) Encyclopedia of Systems Biology. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9863-7_1276
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DOI: https://doi.org/10.1007/978-1-4419-9863-7_1276
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