3 ms·

This title seems a bit confusing, since PCA is a form of unsupervised feature selection (or rather, feature weighting). The title seems like it has the form "<

• notafraudster 8y ago
  I tend to think of feature selection as being methods like LASSO that induce conceptual sparsity in the feature space; and use the label "dimensional reduction" for methods which reduce covariant dimensionality without inducing conceptual sparsity -- PCAing 100 features down to 3 principal components may or may not actually lend itself to a simpler interpretation but often it just susbstitutes the problem of labeling loaded principal components in lieu of the original problem. Not disagreeing with you, just spitballing.
  • closed 8y ago
    That's fair and was helpful to hear! I had dimensionality reduction in the back of my mind, and now that you mention it, your point about conceptual sparsity definitely seems important here.