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But this is not so much "feature selection" as it is "compressing the data". It says right in the conclusion that the entire goal was "dimensionality reduction"
by hervature 5y ago
But this is not so much "feature selection" as it is "compressing the data". It says right in the conclusion that the entire goal was "dimensionality reduction". In a very real way, PCA is selecting all features. That is, your data collection process remains unchanged. In a real feature selection, you would be able to say "ok, we don't need to collect X data anymore".
- barrkel 5y agoThe point of the article, though, is that dimensionality reduction which minimizes information loss (PCA) isn't necessarily dimensionality reduction which minimizes signal loss. A good example from the article is random features: random implies high information content but no signal value.
- platz 5y agoexactly right, barrkel. Did you read the article, hervature?