Talk:Principal components analysis

We probably need a small article on the arg max and arg min notations.

The article seems to be missing crucial details. I can't see where the actual dimension reduction is happening. Is the idea that you have several samples of the measurement vector x and you use these to estimate the expectations? 130.188.8.9 16:49, 20 Aug 2003 (UTC)

- There should now be a clue. However, the article still needs work

Principle components analysis is better known as Principle component analysis (singular). This should be the main title and the plural form a synonym referring to this page (Unfortunately I do not know how to do it).

I've always heard it with the plural. I have a PhD in statistics. I'm not saying the singular could never be used, but the plural is certainly the one that's frequently heard. Michael Hardy 21:18, 22 Mar 2004 (UTC)

The only monography solely dedicated to PCA is from Jolliffe to my knowledge and is titled "Principal component analysis". The naming issue is discussed in the introduction otherwise than you indicate. Then again naming issues are conventions and vary across the globe. Sboehringer

Google says: "Principal component analysis": 103,000 hits, "Principal components analysis": 46,300 hits. MH 13:48, 25 Mar 2004 (UTC)

I have that monograph and you are correct. It seems, however, that the analysis elucidates the principal components, plural, and so unless one is only interested in one principal component at a time, the plural appears to be more appropriate.

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Moving Michael Hardy's comments to Talk:

This article needs some serious revamping, to say the least. One cannot assume without loss of generality that the expectation is zero. If the expectation were observable, one could subtract it from x and get something with zero expectation, and so no generality would be lost by this assumption. In practice the expecation is never observable, and one must consider the probability distribution of the difference between x and an estimate, based on data, of the expectation of x.

Excuse me, but that is absurd. If the mean were observable, then one could simply subtract the mean from X, getting something with zero mean, and then indeed no generality would be lost by assuming that. In practice, one must use a data-based and therefore uncertain estimate of the mean, and one must therefore consider the probability distribution of the difference between X and the estimate of the mean of X.

If I may respond --- PCA is a technique that is applied to empirical data sets. PCA eigendecomposes the maximum likelihood covariance matrix. Indeed, there is a distribution of PCA decompositions about the "true" decomposition that you would get in the infinite data limit. But, that does not make it absurd. Or rather, no more absurd than any other maximum likelihood estimate. Any ML technique will have a variance around the estimate from infinite data.

Are you objecting because ML is not mentioned in the article? Or is it something else? -- hike395 04:39, 5 May 2004 (UTC)

Something else. Several something elses. It doesn't seem like that good an article. I'll probably drastically edit it within a few months; it's on my list. Michael Hardy 16:31, 5 May 2004 (UTC)

PCR and PLS?

would it be redundant to include some discussion of principal components regression? i don't think so, but i don't feel qualified to explain it.

It would also be nice to have a piece on Partial Least Squares. Geladi and Kowalski Analytica Chimica Acta 185 (1986) 1-17 may serve as a starting point.

I disagree --- PLS and PCR are both forms of linear regression, which is supervised learning. PCA is density estimation, which is unsupervised learning. Very different sorts of algorithms --- hike395 04:35, 22 Mar 2005 (UTC)