Monday, October 10, 2011

Reviews and unsupervised feature-selectors

a) Papers: In case you want to take a look at some of the recent reviews we have submitted, here are two papers: 1) kernel density estimation and 2) ensemble methods.

Another one with 40% of the reviews done is about active learning (here).

b) QUICK+ (under implementation): An unsupervised instance and feature selector, which works in two steps:

  • Step1: Run QUICK on the dataset and find how many instances (q) you need.
  • Step2: 
    • For q instances, transpose the dataset and find the popularity of features in a space defined by instances. 
    • Throw away features from least popular to most popular until there is a statistically significant deterioration in performance.
    • Stop when performance drops significantly and put the last-thrown feature back.

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