PPT-A m inimal subspace rotation

Author : trish-goza | Published Date : 2018-11-10

approach for obtaining stable amp accurate loworder projectionbased reduced order models for nonlinear compressible flow Irina Tezaur 1 Maciej Balajewicz 2 1

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A m inimal subspace rotation: Transcript

approach for obtaining stable amp accurate loworder projectionbased reduced order models for nonlinear compressible flow Irina Tezaur 1 Maciej Balajewicz 2 1 Quantitative Modeling amp Analysis Department Sandia National Laboratories. . Mardani. , Gonzalo . Mateos. and . Georgios. . Giannakis. ECE Department, University of Minnesota. Acknowledgment. : . AFOSR MURI grant no. FA9550-10-1-0567. Vancouver, Canada. May 18, 2013. Rank Minimization for Subspace Tracking from Incomplete Data. T y = In the plane, the space containing only the zero vector and any line through the origin ar n 12 5 into two perpendicular subspaces. For A = 2 4 10 , the row space has 1 dimension 1 and basi M. Soltanolkotabi E.Elhamifar E.J. Candes. 报告. 人：万晟、元玉慧. 、. 张. 驰. 昱. 信息科学与技术学院. 智. 能科学系. 1. Main Contribution. Existing work. Subspace Clustering. Real Vector Spaces. Subspaces. Linear Independence. Basis and Dimension. Row Space, Column Space, and Nullspace. Rank and Nullity. 2. 5-2 Subspaces. A . subset. . W. of a vector space . V. is called a . Asymptotics. Yining Wang. , Jun . zhu. Carnegie Mellon University. Tsinghua University. 1. Subspace Clustering. 2. Subspace Clustering Applications. Motion Trajectories tracking. 1. 1 . (. Elhamifar. 112113 INIMAL PE INIMAL G INIMAL INIMAL FIL INIMAL FIL INIMAL INIMAL INIMAL PELA INIMAL PELA PEQ8 PE PEQ6 INIMAL G INIMAL G INIMAL G PE INIMAL CA PEQ7 INIMAL CA INIMAL PEQ0 -PALLADIO CA Zeev . Dvir. (Princeton). Shachar. Lovett (IAS). STOC 2012. Subspace evasive sets. is . (. k,c. ) subspace evasive. if for any k-dimensional linear subspace V, . Motivation. is . mechanics. Irina Tezaur. 1. , . Maciej. Balajewicz. 2. 1. Extreme Scale Data Science & Analytics Department, Sandia National Laboratories. 2. Aerospace Engineering Department, University of Illinois Urbana-Champaign. W. of a vector space . V. . Recall:. Definition: . The examples we have seen so far originated from considering the span of the column vectors of a matrix . A. , or the solution set of the equation. Yining Wang. , Yu-Xiang Wang, . Aarti. Singh. Machine Learning Department. Carnegie . mellon. university. 1. Subspace Clustering. 2. Subspace Clustering Applications. Motion Trajectories tracking. 1. mechanics. Irina Tezaur. 1. , . Maciej. Balajewicz. 2. 1. Extreme Scale Data Science & Analytics Department, Sandia National Laboratories. 2. Aerospace Engineering Department, University of Illinois Urbana-Champaign. René Vidal. Center for Imaging Science. Institute for Computational Medicine. Johns Hopkins University. Data segmentation and clustering. Given a set of points, separate them into multiple groups. Discriminative methods: learn boundary. A Deterministic Result. 1. st. Annual Workshop on Data Science @. Tennessee . State University. 1. Problem Definition . (. Robust Subspace Clustering). input. output. white noise. outliers. m. issing entries. Venkat. . Guruswami. , Nicolas Resch and . Chaoping. Xing. Algebraic . Pseudorandomness. Traditional pseudorandom objects (e.g., . expander graphs. , . randomness extractors. , . pseudorandom generators.

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