Timeline for How does PCA improve the accuracy of a predictive model?
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| May 11, 2020 at 19:00 | comment | added | EngrStudent | Whitening or pinkening transforms are both PCA approaches that retain all the components. The difference is trying to transform the data so that the diagonal of the covariance is more uniform, or less uniform. | |
| Jan 25, 2017 at 9:28 | comment | added | amoeba | "In theory the PCA makes no difference": This is only the case when all PCs are retained. Usually when people talk about doing PCA prior to some other algorithm they mean that they keep only a small subset of PCs (and the OP wrote about "compressing data" too). Your answer does not cover this possibility at all so I feel like it does not really address the question. | |
| Jun 30, 2016 at 23:45 | comment | added | KarthikS | This conversation is so funny, much more than the quote itself. I had once asked my professor if I can cite a cited quote because the original paper was a phantom one! | |
| Apr 3, 2013 at 20:56 | comment | added | EngrStudent | Cardinal - He is not the original source, but he is the source that I heard it from. I saw him presenting something onstage in 2009. The only thing I retain, 4 years later, is this quote. | |
| Apr 3, 2013 at 19:13 | comment | added | cardinal | Aside: I would be interested to see an accurate and reputable sourcing of your introductory quote. It is variously attributed to several people on the internet, most recognizably, Yogi Berra and Albert Einstein. I have personally heard it from an engineer that is a generation older than Perlmutter and this was long enough ago that it makes me highly doubt that Perlmutter could be the original source. | |
| Apr 3, 2013 at 15:08 | history | answered | EngrStudent | CC BY-SA 3.0 |