Purely Optional/Extra-Credit Assignment

by Drew Bagnell on December 3, 2009

*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


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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