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.