Timeline for Making sense of principal component analysis, eigenvectors & eigenvalues
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
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| Jul 15, 2013 at 22:50 | history | edited | Christian Bueno | CC BY-SA 3.0 |
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| Jul 15, 2013 at 22:50 | comment | added | Christian Bueno | I must admit, I'm embarrassed to have forgotten, but good point about covariance matrices being diagonalizable in general. I'm going to edit to reflect that. Also, could you elaborate on point (2)? I'm not familiar with the difference between parametric or non-parametric procedures. | |
| Jul 15, 2013 at 21:04 | comment | added | whuber♦ | Thank you for your contribution. It seems to address an unnecessarily narrow interpretation of PCA, however. (1) PCA has been fruitfully applied to highly non-Gaussian datasets. (2) PCA is not a formal parametric procedure; it perhaps is better to think of it as exploratory in spirit. (3) All covariance matrices, of any kind of multivariate distribution or data, are diagonalizable. Neither Gaussianity (Normality) nor non-degeneracy are requirements. (Symmetry of the matrix and having real components guarantee diagonalizability.) | |
| S Jul 15, 2013 at 19:54 | review | Late answers | |||
| Jul 15, 2013 at 19:54 | |||||
| S Jul 15, 2013 at 19:54 | review | First posts | |||
| Jul 15, 2013 at 20:05 | |||||
| Jul 15, 2013 at 19:37 | history | answered | Christian Bueno | CC BY-SA 3.0 |