| Author | Message | ||
     Howard Mark (hlmark) Senior Member Username: hlmark Post Number: 431 Registered: 9-2001 |
Jerry - I went to that page that you send the link to, and while the videos were very nice, I didn't get any audio. I felt the lack, because several of the sequences shown needed some explanation, and without audio there was no explanation available. Howard \o/ /_\ | ||
| Howard Mark (hlmark) Senior Member Username: hlmark Post Number: 429 Registered: 9-2001 |
Jerry - I watched a couple of those videos. They were very nice but there was no audio narration. It seemed to me there should have been some audio, is there some setting for the player that I needed to change to turn the audio on? Howard \o/ /_\ | ||
| Jerry Jin (jcg2000) Senior Member Username: jcg2000 Post Number: 42 Registered: 1-2009 |
Hi, there For anyone who wants a intuitive understanding of latent variable based models, here are something you may like: http://models.life.ku.dk/~movies/ Jerry Jin | ||
| Bruce H. Campbell (campclan) Moderator Username: campclan Post Number: 128 Registered: 4-2001 |
A recent article in New Scientist (May21, 2011, p 18) relates a presentation at a conference on robotic sensing. The robot can distinguish between articles of clothing using principal component analysis. I find this interesting with respect to NIR. Does PCA work in N dimensions to first limit the overall space of the object (spectra) and then further define the spectral characteristics to arrive at a desired answer? I am looking at this with respect to geometry, rather than the underlying mathematical equations. Or is there another approach to understanding, at least a little more, about PCA that is not the equations? |