Timeline for How does PCA represent all data with just a few principal components? [duplicate]
Current License: CC BY-SA 3.0
17 events
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| Jan 24, 2017 at 16:22 | history | closed |
Michael R. Chernick amoeba whuber♦ |
Duplicate of Making sense of principal component analysis, eigenvectors & eigenvalues | |
| Jan 24, 2017 at 3:21 | comment | added | bbadyalina | @gung how that the first few principle component can represent a higher dimensional dataset well enough? that more likely my question. | |
| Jan 24, 2017 at 3:20 | vote | accept | bbadyalina | ||
| Jan 24, 2017 at 3:12 | vote | accept | bbadyalina | ||
| Jan 24, 2017 at 3:20 | |||||
| Jan 24, 2017 at 3:12 | vote | accept | bbadyalina | ||
| Jan 24, 2017 at 3:12 | |||||
| Jan 24, 2017 at 2:52 | answer | added | SmallChess | timeline score: 1 | |
| Jan 24, 2017 at 2:49 | answer | added | Abhinav Gupta | timeline score: 0 | |
| Jan 24, 2017 at 2:46 | comment | added | gung - Reinstate Monica | I'm still not sure exactly what you mean. Are you referring to a situation where you have fewer observations than dimensions (n<p) & why that means there will necessarily be fewer principle components than p? Or are you asking how it can be possible that you can perfectly fit a dataset with fewer than p principle components even when n>>p? Or how it is that the first few principle components can represent a higher dimensional dataset 'well enough'? Or something else? | |
| Jan 24, 2017 at 2:44 | comment | added | Carl | I think the answer is that PCA finds the more contributory independent variables. | |
| Jan 24, 2017 at 2:43 | review | Close votes | |||
| Jan 24, 2017 at 11:28 | |||||
| Jan 24, 2017 at 2:35 | comment | added | Carl | @MichaelChernick Mostly a fixable grammar and sentence structure problem. | |
| Jan 24, 2017 at 2:30 | history | edited | Carl | CC BY-SA 3.0 |
added 29 characters in body; edited title
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| Jan 24, 2017 at 2:29 | vote | accept | bbadyalina | ||
| Jan 24, 2017 at 3:12 | |||||
| Jan 24, 2017 at 2:23 | comment | added | Michael R. Chernick | The question is not clear. | |
| Jan 24, 2017 at 2:19 | review | Low quality posts | |||
| Jan 24, 2017 at 2:30 | |||||
| Jan 24, 2017 at 2:14 | comment | added | Frank Drost | I am not certain that I understand your question. You have a dataset with many observations but when you use PCA you end up with less components then that there are observations? If that is the case, please see this question and answer: stats.stackexchange.com/questions/99351/… | |
| Jan 24, 2017 at 2:03 | history | asked | bbadyalina | CC BY-SA 3.0 |