*No groups*– individual assignments ony. Due Dec. 12 by noon EST.
Take the existing HW4 data-set (or alternately a labeled ladar data-set you prefer) you have used for classification and explore two things with it:
1 ) Implement exponentiated gradient descent or an L1 regularized method (or both simultaneously) on a loss function you have already implemented. (Log loss, hinge loss, squared loss etc…)
Are the exponential gradient algorithms good performers? Take the current feature set and:
- Add a large number of random features
- Add a large number of features that are noisy, corrupted versions of the features already in the
data-set.
How do the various methods perform in these situations? Compare and contrast with l2 methods.
2) Implement a technique for “contextual classification”. Possible options:
a) Implement the graph cut method in http://www.ri.cmu.edu/publication_view.html?pub_id=6297
b) Implement the multiple k-means clustering of data-points/voting scheme pioneered in http://www.cs.uiuc.edu/homes/dhoiem/publications/Hoiem_Geometric.pdf and use features generated from that. (For Discussed of ladar points see http://www.ri.cmu.edu/publication_view.html?pub_id=6297)
c) Put a continuous valued random field using, e.g., the l1 Total Variation norm between data-points. Using optimization to find the optimal assignment for each ladar point. Feel free to do 2 classes only here.
d) Propose some other method to use multiple related/nearby labels to improve the structured prediction.
Can you get improvements on this data-set? Why or why not?
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