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Building Machine Learning Systems with Python - Second Edition by Luis Pedro Coelho, Willi Richert

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Multidimensional scaling

Although, PCA tries to use optimization for retained variance, multidimensional scaling (MDS) tries to retain the relative distances as much as possible when reducing the dimensions. This is useful when we have a high-dimensional dataset and want to get a visual impression.

MDS does not care about the data points themselves; instead, it's interested in the dissimilarities between pairs of data points and interprets these as distances. The first thing the MDS algorithm is doing is, therefore, taking all the Multidimensional scaling datapoints of dimension and calculates a distance matrix using a distance function , which measures the (most of the ...

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