Monthly Archives: January 2018

Data Science Done Right (the Kitchen Style) #13

On possible criteria for partitioning PCA eigen space to the “error” and “linear modeling” subspaces As we mentioned in previous chapters, by performing PCA, we implicitly find the least lossy linear models of the hidden variables that describe our data, … Continue reading

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Data Science Done Right (the Kitchen Style) #12

Example of using PCA for signal separation problem Before going into more details of more sophisticated multidimensional scaling techniques, and criteria for selecting principal components/factors, let’s take a look at data that has the same measurement units in all their … Continue reading

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