Day 9

This morning we looked at some of the interns' abstracts. This showed me that I should probably edit my own abstract. It's really quite difficult to maintain brevity (recommended <100 words) while trying to include everything I think I should. This morning's peer review helped me sort it out a bit more though.
Today was Ron's first day back, and he had a lot of catching up to do with Angelina, Marc, and I. He showed me how to use RIT Libraries and Google Scholar to find papers about semi-supervised models to compare our own performance to. This task basically took up all of my time today, and I'm likely going to do something similar tomorrow. The process is really tedious, but also clearly necessary for the paper I am helping Ron with. I'd compare it to trying to find a needle in a haystack, but finding a handful of paperclips instead. Not quite what you're looking for, but it'll have to do until you find a better one.
As promised from last time, I have another data visualization. Each "pixel" in the Pavia University image consists of 103 values, representing how much the material at said pixel reflects each wavelength. These values can be plotted like so, and it can be pretty easy to make some generalizations about the given material.

The following image shows 9 graphs, each representing one of Pavia University's 9 classes. These plots can be thought of as hundreds or thousands of the above plot stacked on top of each other. This gives a pretty good idea of the general reflectance of each material and more importantly how varied they can be.

Comments

Post a Comment

Popular posts from this blog

Outline

Motivation/Background: HSI classification What is HSI? Comparison to RGB The advantages of HSI over other kinds of data Instant, remote, nondestructive data collection Hundreds of data points per pixel What is classification? Introduce using toy data set, like CUB-200 Applications of HSI classification Cancer detection Art authentication Food quality analysis Remote sensing Urban development agricultural monitoring environmental assessment Problems faced High dimensionality Scarcity of labeled data Method: MCAE and Ladder Network Feature Extractors Baseline: PCA Inspiration: Autoencoder Our proposed method: MCAE Classifiers Baseline: SVM Inspiration: Neural network Our proposed method: Ladder network Results : State-of-the-art Comparison Comparison of MCAE against PCA and state-of-the-art Comparison of Ladder Network against SVM and state-of-the-art
Read more

Day 2

Day 2 started with a very brief meeting where we went over the schedule for the next six weeks. Abstract, outline, presentation; two weeks per task. It was at this point that I realized the difficulty of making technical topics sound interesting, then I immediately dismissed the problem as one for my future self. Today was dedicated to Support Vector Machine (SVM) creation, evaluation, and optimization. I was unaware of many of the wonderful functions included in the provided libraries, and ended up doing the data reformatting less elegantly. It still worked, so I left it. When I finished up my SVM code, I tried to help Nate with the errors he was running into. The keyword here is "tried." Note that it's not easy to explain concepts that you figured out hours earlier and happen to be more complicated than they should be. Nate was a good sport about it though, and with Ron's help, he had a good idea of the solution by the end of the day. For the last part of the day,
Read more

Day 28

Week 6 of the internship; the final presentation is on Thursday. It doesn't feel like week 6, but that's probably because I missed almost all of week 4. Regardless, there was much work to do before Thursday, so Nate and I rehearsed our presentations twice in front of the lab. Many revisions were made, and I'm feeling much more prepared for the real deal. Thank you to everybody who was willing to listen to four lame high school presentations about the same topic in the same day. Your contribution will make the very same presentations significantly less lame on Thursday. Apart from presentation-related stuff, Ron and I ran some ladder network and SMCAE experiments all day. Ron tackled the ladder network with PELU activation (which was apparently very difficult) and I continued with adjusting initial layer sizes on the ladder network and doing SMCAE runs with fixed component count PCA as a feature scaler. The results aren't looking too hot, but I'll withhold judgem
Read more