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Erschienen in:
2018 | OriginalPaper | Buchkapitel
6. Dimensionality Reduction
verfasst von : Ivo D. Dinov
Erschienen in: Data Science and Predictive Analytics
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Abstract
Now that we have most of the fundamentals covered in the previous chapters, we can delve into the first data analytic method, dimension reduction, which reduces the number of features when modeling a very large number of variables. Dimension reduction can help us extract a set of “uncorrelated” principal variables and reduce the complexity of the data. We are not simply picking some of the original variables. Rather, we are constructing new “uncorrelated” variables as functions of the old features.