Abstract
Principal component analysis (PCA) is the problem of fitting a low-dimensional affine subspace to a set of data points in a high-dimensional space. PCA is, by now, well established in the literature, and has become one of the most useful tools for data modeling, compression, and visualization.
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Vidal, R., Ma, Y., & Sastry, S. S. (2016). Principal component analysis. In Interdisciplinary Applied Mathematics (Vol. 40, pp. 25–62). Springer Nature. https://doi.org/10.1007/978-0-387-87811-9_2
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