Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature sel...
We propose a novel application of the Simultaneous Orthogonal Matching Pursuit (SOMP) procedure to perform variable selection in ultra-high dimensional multiple output regression ...
We consider parameterized convex optimization problems over the unit simplex, that depend on one parameter. We provide a simple and efficient scheme for maintaining an -approximat...
There are many situations in which we have more than one view of a single data source, or in which we have multiple sources of data that are aligned. We would like to be able to bu...
Many scalable data mining tasks rely on active learning to provide the most useful accurately labeled instances. However, what if there are multiple labeling sources (`oracles...