Abstract
Principal component analysis (PCA) is generally considered to be a tool to visualize the relationship between sample objects as a statistical tool especially when the number of features attributed to individual samples is too huge to interpret. Mathematically, PCA is nothing but a linear projection of objects in high dimensional space onto low dimensional space. Alternatively, PC can be considered to be a tool that performs feature extraction (FE), because principal components (PC) that PCA generates can be used as new features attributed to individual objects. In this chapter, I would like to add one more function to PCA, feature selection. I demonstrate how we can make use of PCA in order to select features and how well it works in which situations. This can be also a good introduction for TD based unsupervised FE, which is in some sense the extension of the method proposed in this chapter.
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Hartigan, J.A., Hartigan, P.M.: The dip test of unimodality. Ann. Stat. 13(1), 70–84 (1985). https://doi.org/10.1214/aos/1176346577
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Taguchi, Yh. (2020). PCA Based Unsupervised FE. In: Unsupervised Feature Extraction Applied to Bioinformatics. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-22456-1_4
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DOI: https://doi.org/10.1007/978-3-030-22456-1_4
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