News for hackers. Stuff that matters.
Re:Fastplotlib: GPU-accelerated, fast, and interactive plott... by macleginn on Tuesday March 11, 2025 @02:51PM (#43335795)
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.
Re:Fastplotlib: GPU-accelerated, fast, and interactive plott... by wtallis on Tuesday March 11, 2025 @02:55PM (#43335835)
> 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.)

Re:Fastplotlib: GPU-accelerated, fast, and interactive plott... by fpl-dev on Tuesday March 11, 2025 @04:21PM (#43336607)
Exactly that's a good example!
Re:Fastplotlib: GPU-accelerated, fast, and interactive plott... by mturmon on Tuesday March 11, 2025 @06:20PM (#43337723)
I 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)

So this is not unheard of for time series analysis.