IEEE Computer Society Nanjing Chapter年会:Large-Scale Structured Sparse Learning
Jieping Ye
Associate Profesor
Arizona State University
abstract:
Recent advances in high-throughput technologies have unleashed a torrent of
data with a large number of dimensions. Examples include gene expression
pattern images, gene/protein 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. Finally, we introduce the
SLEP package recently developed in our group for large-scale sparse
learning.
bio:
Jieping Ye is an Associate Professor of the Department of Computer Science
and Engineering at Arizona State University. He received his Ph.D. in
Computer Science from University of Minnesota, Twin Cities in 2005. His
research interests include machine learning, data mining, and biomedical
informatics. He won the outstanding student paper award at ICML in 2004, the
SCI Young Investigator of the Year Award at ASU in 2007, the SCI Researcher
of the Year Award at ASU in 2009, the NSF CAREER Award in 2010, the KDD best
research paper award honorable mention in 2010, and the KDD best research
paper nomination in 2011.