Principal component analysis

Principal component analysis (PCA) is a technique that helps define a smaller and more relevant set of features. The new features obtained from PCA are linear combinations (that is, rotation) of the current features, even if they are binary. After the rotation of the input space, the first vector of the output set contains most of the signal's energy (or, in other words, its variance). The second is orthogonal to the first, and it contains most of the remaining energy; the third is orthogonal to the first two vectors and contains most of the remaining energy, and so on. It's just like restructuring the information in the dataset by aggregating as much as possible of the information onto the initial vectors produced ...

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