Timeline for Loadings vs eigenvectors in PCA: when to use one or another?
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Dec 25, 2017 at 11:40 | comment | added | ttnphns | Brief summary of methods of FA on this site. They all do some sort of eigenanalysis, but not all do that of correlation/covariance matrix, like PCA does it. | |
| Dec 25, 2017 at 11:10 | comment | added | Matti Wens | Apologies, I don't think there is a publicly available version of my paper, although you can get access through Deepdyve.com with a two-week trial. The first chapter of Malinowski's book is available from the link above. This covers the basics without mentioning eigenanalysis. I must admit that I was unaware that factor analysis could be done without eigenanalysis, as the variant I have used - target factor analysis - does. | |
| Dec 25, 2017 at 9:47 | comment | added | ttnphns | OK, maybe your account is a special still valid one - I can't say w/o reading the sources you offer. Yet, I'd remark that the "relationship" between loadings and eigenvectors in PCA is all in its formula placed in the question; so there is hardly anything to "explain" (explained should be the different utility of them). Another thing to remark is that the Q is primarily about PCA, not FA. And, in the end, not every FA method deals with eigenvectors at all, while it necessarily deals with loadings. | |
| Dec 25, 2017 at 8:42 | comment | added | Matti Wens | My research on the subject is summarised in this paper: onlinelibrary.wiley.com/doi/10.1002/sia.740231303/full | |
| Dec 25, 2017 at 8:23 | comment | added | Matti Wens | I consider the material I have posted to be relevant to the discussion in this thread, and it offers one explanation of the relationship between loadings and eigenvectors. | |
| Dec 25, 2017 at 1:30 | comment | added | ttnphns | This answer seems to have several problems. First, check your formulas, please, they are not correct. Second, you are trying to discuss differences between FA and PCA. We have a separate long thread on CV for that, while the current thread is about loadings vs eigenvectors, so the answer is misplaced. Third, your picture of FA is distorted, especially in phrases such as "the purpose of FA is to decompose D" or "the object of FA is to transform the abstract components into meaningful factors". | |
| Dec 24, 2017 at 21:27 | history | edited | Matti Wens | CC BY-SA 3.0 | Extended and amended after swapping to proper keyboard from phone |
| Dec 24, 2017 at 18:00 | review | Late answers | |||
| Dec 24, 2017 at 18:16 | |||||
| Dec 24, 2017 at 17:45 | history | answered | Matti Wens | CC BY-SA 3.0 |