Recent advances in high-throughput technologies have unleashed a torrent of data with a large number of dimensions. Examples include gene expression pattern images, microarray gene expression data, and neuroimages. Variable selection is crucial for the analysis of these data. In this talk, we consider the structured sparse learning for variable selection where the structure over the features can be represented as a hierarchical tree, an undirected graph, or a collection of disjoint or overlapping groups. We show that the proximal operator associated with these structures can be computed efficiently, thus accelerated gradient techniques can be applied to scale structured sparse learning to large-size problems. We demonstrate the efficiency and effectiveness of the presented algorithms using synthetic and real data.
Wednesday, November 10, 2010
Free and open to the public