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Eigendecomposition of the covariance matrix, essentially PCA, is probably the first non-trivial step in the analysis of any dataset. The idea in the comment abo
by macleginn 2y ago
Eigendecomposition of the covariance matrix, essentially PCA, is probably the first non-trivial step in the analysis of any dataset. The idea in the comment above seems to be that it's more useful to combine some basic knowledge of statistics with simpler visualisation techniques, rather than to quickly generate thousands of shallower plots. Being able to generate thousands of plot is useful, of course, but I would agree that promoting good data-analysis culture is more beneficial.
- wtallis 2y ago> Eigendecomposition of the covariance matrix, essentially PCA, is probably the first non-trivial step in the analysis of any dataset For a sufficiently narrow definition of "dataset", perhaps. I don't think it's the obvious step one when you want to start understanding a time series dataset, for example. (Fourier transform would be a more likely step two, after step one of actually look at some of your data.)
- fpl-dev 2y agoExactly that's a good example!
- deleted 2y ago[deleted]
- mturmon 2y agoI agree, but: the technique of “singular spectrum analysis” is pretty much PCA applied to a covariance matrix resulting from time-lagging the original time series. (https://en.wikipedia.org/wiki/Singular_spectrum_analysis https://en.wikipedia.org/wiki/Singular_spectrum_analysis) So this is not unheard of for time series analysis.