Yeah.
OK.
Uh , I mean , you don't have to but  but  but , uh , Hynek has. Um , but it 's also , uh   Hyn - when Hynek 's had people do this kind of LDA analysis , they 've done it on frequency direction and they 've done it on the time direction. I think he may have had people sometimes doing it on both simultaneously  some two - D  and that would be the closest to these Gabor function kind of things. Uh , but I don't think they 've done that much of that. And , uh , the other thing that 's interesting  the  the , uh  the feature selection thing , it 's a simple method , but I kinda like it. Um ,  there 's a   a old , old method for feature selection. I mean ,  eh , uh , I remember people referring to it as old when I was playing with it twenty years ago , so I know it 's pretty old , uh , called Stepwise Linear Discriminant Analysis in which you  which  I think it 's used in social sciences a lot. So , you  you  you  you pick the best feature. And then  you take  y you find the next feature that 's the best in combination with it. And then so on and so on. And what  what Michael 's describing seems to me much , much better , because the problem with the stepwise discriminant analysis is that you don't know that  you know , if you 've  picked the right set of features. Just because something 's a good feature doesn't mean that you should be adding it. So ,  um ,  uh , here at least you 're starting off with all of them , and you 're  throwing out useless features. I think that 's  that seems , uh   that seems like a lot better idea. Uh , you 're always looking at things in combination with other features. Um , so the only thing is , of course , there 's this  this artificial question of  of , uh ,  exactly how you  how you a how you assess it and if  if your order had been different in throwing them out. I mean , it still isn't necessarily really optimal , but it seems like a pretty good heuristic. So I th I think it 's  it 's  I think it 's kinda neat stuff.
Hmm.