PPT-Subspace evasive sets

Author : alida-meadow | Published Date : 2017-04-19

Zeev Dvir Princeton Shachar Lovett IAS STOC 2012 Subspace evasive sets is kc subspace evasive if for any kdimensional linear subspace V Motivation is

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Subspace evasive sets: Transcript

Zeev Dvir Princeton Shachar Lovett IAS STOC 2012 Subspace evasive sets is kc subspace evasive if for any kdimensional linear subspace V Motivation is . Yisong Yue . Carnegie Mellon University. Joint work with. Sue Ann Hong (CMU) & Carlos . Guestrin. (CMU). …. Sports. Like!. Topic. # Likes. # Displayed. Average. Sports. 1. 1. 1. Politics. Ambroziak. Ryan Fox. Cs 638-1. 5/3/10. Virtual Barber. The Goal. Go From This. The Goal. Go From This. To This. The Motivation. For people who have had facial hair for a long time, the decision to shave can be difficult. Evasive Functions. Boaz . Barak, . Nir. . Bitansky. , Ran Canetti,. Yael Tauman . Kalai, . Omer . Paneth, . Amit Sahai. Program Obfuscation . Obfuscated Program. Approved . Document . Signature . Obfuscation. . 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. Accelerating Contextual Bandits. Yisong . Yue, . Sue Ann Hong . and. . Carlos Guestrin . Personalized Recommender Systems. Every day, user . visits . news portal. Wish to personalize to her . preferences. 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 . Yining Wang. , Yu-Xiang Wang, . Aarti. Singh. Machine Learning Department. Carnegie . mellon. university. 1. Subspace Clustering. 2. Subspace Clustering Applications. Motion Trajectories tracking. 1. via Subspace Clustering. Ruizhen. Hu . Lubin. Fan . Ligang. Liu. Co-segmentation. Hu et al.. Co-Segmentation of 3D Shapes via Subspace Clustering. 2. Input. Co-segmentation. Hu et al.. Moritz . Hardt. , David P. Woodruff. IBM Research . Almaden. Two Aspects of Coping with Big Data. Efficiency. Handle. enormous inputs. Robustness. Handle . adverse conditions. Big Question: Can we have both?. René Vidal. Center for Imaging Science. Institute for Computational Medicine. Johns Hopkins University. Manifold Clustering with Applications to Computer Vision and Diffusion Imaging. René Vidal. Center for Imaging Science. 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. David Fleet. We need many clusters. Increasing . number of . clusters. Problem: . Search time, storage . cost . (subspace 1). (subspace 2). (subspace 1). (subspace 2). (subspace 1). (subspace 2). (subspace 1). via Subspace Clustering. Ruizhen. Hu . Lubin. Fan . Ligang. Liu. Co-segmentation. Hu et al.. Co-Segmentation of 3D Shapes via Subspace Clustering. 2. Input. Co-segmentation. Hu et al..

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