About the program: 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 web pages, amino acid sequences describing proteins, text containing news that could influence the price of stocks, 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 in bioinformatics and finance. First, an approach for classification problems using heterogeneous information sources will be presented, within the unifying learning framework of kernel methods. Second, a direct method for sparse principal component analysis will be proposed.
About the Speaker:
Gert Lanckriet is an assistant professor in the Electrical and
Computer Engineering Department at the
Time/Place: Thursday Feb 15, 6:00 P.M. Lockheed
Martin, 4770 Eastgate Mall
Free for
IEEE members, $5 otherwise.
Reservations/Information: Andrew Diamond (IEEE CIS San Diego
Chapter Chair) (858) 509-3115, adiamond@EnvisionSystemsLLC.com