5 ms·
So would this be a more-or-less correct tl;dr: Dimensionality reduction is a compressing data in a way that retains the most important information for the task
by dkersten 5y ago
So would this be a more-or-less correct tl;dr:
Dimensionality reduction is a compressing data in a way that retains the most important information for the task
Feature selection is removing unimportant information (keeping/collecting, or selecting, only the important parts)
Both cut down on the amount of data you end up with, but one does it by finding a representation that is smaller, the other does it by discarding unnecessary data (or, rather, telling you which data is necessary, so you can stop collecting the unnecessary data).
- naijaboiler 5y agoSo they are functionally doing the same thing, reducing the amount of data used. I find the debate about what we should call it useless and pointless. Understand what it is, what it doing, what it s limitations are, and use it appropriately based on your needs. Done.
- platz 5y agoif you read the actual article, dimensionality reduction by PCA does not retain the most important information about the data
- bllguo 5y agoindeed, predictable and disappointing how the discussion devolved into pedantry. should have been obvious what the author meant (plus it is clarified at the very beginning of the article). I'm not sure if this is a ML practitioner vs. statisticians thing or what
- hervature 5y agoYes, I would say this is entirely correct.
- chombier 5y ago> so you can stop collecting the unnecessary data I think that's the key, thanks. But still, if some inputs are redundant shouldn't this be somehow apparent in the eigen-vectors/values of the covariance matrix (making PCA an indirect feature selection algorithm)?