Sparse and large-scale learning with heterogeneous data

Google Tech Talks September 5, 2006 Gert Lanckriet is assistant professor in the Electrical and Computer Engineering Department at the University of California, San Diego. He conducts research on machine learning, applied statistics and convex optimization with applications in computational biology, finance, music and vision. ABSTRACT An important challenge for the field of machine learning is to deal with the increasing amount of data that is available for learning and to leverage the (also increasing) diversity of information sources, describing these data. Beyond classical vectorial data formats, data in the format of graphs, trees, strings and beyond have become widely available for data mining, e.g., the linked structure of the world wide web, text, images and sounds on web pages, protein interaction networks, phylogenetic trees, etc. Moreover, for interpretability and economical reasons, decision rules that rely on a small subset of the information sources and/or a small subset of the features describing the data are highly desired: sparse learning algorithms are a must. This talk will outline two recent approaches that address sparse, large-scale learning with heterogeneous data, and show some applications.