Day 24

Similar to yesterday, I went about poking the ladder network in hopes of exploiting every percentage of accuracy that it can offer. I really do think that this model has a lot of potential, seeing how well it does with MNIST with only 100 labeled samples. It's just frustratingly difficult to find the magic algorithms and numbers that make it suited for Pavia University. Even if I had a very deep understanding of how it works, nobody really knows how to pick the perfect learning rate, layer sizes, activation functions, etc.

Research is hard.

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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
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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,
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Day 30

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