Timeline for How to interpret PCA on time-series data?
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Nov 26, 2014 at 19:09 | comment | added | statHacker | Thanks for all your help. Is the first principal component weights vector just the mean time series collapsing across all voxels? If it were the mean, it would result in the smallest scores to fit to the individual data traces. | |
| Nov 26, 2014 at 16:07 | comment | added | conjectures | I've re-arranged things. Apologies, was a left over from before I sorted something else out. | |
| Nov 26, 2014 at 16:06 | history | edited | conjectures | CC BY-SA 3.0 | deleted 99 characters in body |
| Nov 26, 2014 at 15:50 | comment | added | statHacker | You have confused me by discussing $\mathbf V$ and $\mathbf S$ in the equation $$\mathbf J = \mathbf U^\top \mathbf Y.$$ Do you mean the first 2 or 3 columns of $\mathbf U$? | |
| Nov 26, 2014 at 15:34 | comment | added | statHacker | Also, you are correct that the original vector $\mathbf Y$ is chopped up to lengths of $\hat {\mathbf T}$ $\n$ | |
| Nov 26, 2014 at 15:26 | vote | accept | statHacker | ||
| Nov 26, 2014 at 15:32 | |||||
| Nov 26, 2014 at 15:26 | comment | added | statHacker | The first figure above refers to an experiment with the same visual stimulus presented every time. There is a different figure and movie for those data. The second figure above refers to a different experiment in which the stimuli are visual stimuli with differing orientations, the traces in the 2nd figure above are colored to simply correspond to differing visual stimuli orientations. | |
| Nov 26, 2014 at 14:33 | history | answered | conjectures | CC BY-SA 3.0 |