Matrix-based Learning Algorithms for Data Mining and Bioinformatics
4.15 10-:00-11:00 (15th April Tuesday 10:00-11:00am)
FIT 1-515
H.Q. Ding
Matrix-based data mining and statistical learning is going through a Renaissance period with many new developments. We describe several major advances in the area. We show that Principal Component Analysis (PCA) provides solutions to K-means clustering, thus connecting dimension reduction to clustering, two fundamental aspects of unsupervised learning.
We describe the state-of-art Laplacian matrix based spectral clustering and their effectiveness results from a self-aggregation property due to the nonlinear mapping. We describe their applications in bioinformatics and social sciences. These advances pave the way to establish a matrix factorization based learning framework, a new powerful direction in data mining. They benefit significantly from matrix knowledges accumulated over centuries and the successful developments of scientific and engineering computing of the last 30 years.
Professor
Department of Computer Science and Engineering University of Texas at Arlington
Email: CHQDing@uta.edu
HP : http://ranger.uta.edu/~chqding/
He joined CSE Department at UT Arlington in the fall of 2007. His main research areas are bioinformatics and machine learning. In 1996-2007, he was a staff computer scientist at Lawrence Berkeley National Laboratory. His work on multi-class protein fold prediction is now standard benchmark for protein 3D strucure prediction. His paper on molecular dynamics simulation algorithm has been cited 241 times according to Science Citation Index. He earned a Ph.D. in 1987 from Columbia University in Theoretical Physics and Computer Science on building a parallel processor using Intel 80286s and commodity FPUs ( Science, front cover story, March 18, 1988), designing algorithms and doing large scale QCD simulations on it. From 1987 to 1993, he worked at California Institute of Technology on Caltech Hypercubes developing parallel algorithms for Materials Science (see Nature article by Editor John Maddox ) and Computational Biology (see a National Research Council Report ). He received a Pfister Fellowship at Columbia (1981-83), two Best Paper Awards for climate data assimilation parallel algorithm and supernova detection using support vector machines, a NASA Group Achievement Award at JPL, and two Outstanding Performance Awards at LBNL. He also served in review panels for National Science Foundation, editorial board for a bioinformatics journal, and program committees for leading conferences in data mining, machine learning and bioinformatics. His papers were cited 1431 times according to Google Scholar.
