Day 3

After today's daily briefing about writing abstracts, Nate and I got to work at the lab. I picked up where I left off with autoencoders and finished the convolutional and denoising ones pretty quickly. I'll probably come back to the more complex autoencoders some other time. Left with a nagging feeling that my SVM code from last time was subpar, I revisited it. I ended up rewriting almost all of it. The results included elegant data manipulation, good runtime, optimized preprocessing, tuned hyperparameters, complete metrics, and (this is the important one) okay accuracy. Not great, but not bad either. I think that under the given constraints, I nearly achieved the best that the SVM could perform, so I'm ready to close the book on this tutorial experiment. That's not actually true; Nate's SVM is having some performance problems that we have yet to diagnose...
I also continued reading about the topics relevant to my experiments. I have to admit: a lot of it still went over my head. There are some details I'll have to straighten out with ladder networks and PCA, but some supplementary material from the internet definitely helped out. I think I should be on track to start work on my experiments the day after tomorrow.

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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
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Day 2

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