PPT-Multivariate Dyadic Regression Trees for Sparse Learning Pr

Xi Chen Machine Learning Department Carnegie Mellon University joint work with Han Liu Content Experimental Results Statistical Property Multivariate Regression and Dyadic Regression Tree. An Introduction &. Multidimensional Contingency Tables. What Are Multivariate Stats?. . Univariate = one variable (mean). Bivariate = two variables (Pearson . r. ). Multivariate = three or more variables simultaneously analyzed . Volkan . Cevher. volkan.cevher@epfl.ch. Laboratory. for Information . . and Inference Systems - . LIONS. . http://lions.epfl.ch. Linear Dimensionality Reduction. Compressive sensing. non-adaptive measurements. From Theory to Practice . Dina . Katabi. O. . Abari. , E. . Adalsteinsson. , A. Adam, F. . adib. , . A. . Agarwal. , . O. C. . Andronesi. , . Arvind. , A. . Chandrakasan. , F. Durand, E. . Hamed. , H. . and decoding. Kay H. Brodersen. Computational Neuroeconomics Group. Institute of Empirical Research in Economics. University of Zurich. Machine Learning and Pattern Recognition Group. Department of Computer Science. Gilad Freedman. Raanan Fattal. Hebrew University of Jerusalem. Background and . o. verview. Algorithm . description. L. ocal . self similarity. Non-dyadic filter bank. Filter . design. Results. Single . Raja . Giryes. ICASSP 2011. Volkan. Cevher. Agenda. The sparse approximation problem. Algorithms and pre-run guarantees. Online performance guarantees. Performance bound. Parameter selection. 2. Sparse approximation. to Multiple Correspondence . Analysis. G. Saporta. 1. , . A. . . Bernard. 1,2. , . C. . . Guinot. 2,3. 1 . CNAM, Paris, France. 2 . CE.R.I.E.S., Neuilly sur Seine, France. 3 . Université. . François Rabelais. Recovery. . (. Using . Sparse. . Matrices). Piotr. . Indyk. MIT. Heavy Hitters. Also called frequent elements and elephants. Define. HH. p. φ. . (. x. ) = { . i. : |x. i. | ≥ . φ. ||. x||. p. . Michael Elad. The Computer Science Department. The Technion – Israel Institute of technology. Haifa 32000, Israel. MS45: Recent Advances in Sparse and . Non-local Image Regularization - Part III of III. models for fMRI . data. Klaas Enno Stephan. (with 90% of slides kindly contributed by . Kay H. Brodersen. ). Translational . Neuromodeling. Unit (TNU). Institute for Biomedical Engineering. University . Author: . Vikas. . Sindhwani. and . Amol. . Ghoting. Presenter: . Jinze. Li. Problem Introduction. we are given a collection of N data points or signals in a high-dimensional space R. D. : xi ∈ . Weifeng Li and . Hsinchun. Chen. Credits: Hui Zou, University of Minnesota. Trevor Hastie, Stanford University. Robert . Tibshirani. , Stanford University. 1. Outline. Logistic Regression. Why Logistic Regression?. Pablo Aldama, Kristina . Vatcheva. , PhD. School of Mathematical & Statistical Sciences, University of Texas Rio Grande Val. ley. Data mining methods, such as decision trees, have become essential in healthcare for detecting fraud and abuse, physicians finding effective treatments for their patients, and patients receiving more affordable healthcare services (. Dana Randall. Georgia Institute of Technology. Joint . with: Sarah . Cannon and Sarah Miracle. Rectangular Dissections. Rectangular dissections arise in:. VLSI layout. Mapping graphs for floor layouts .