PPT-Robust Subspace Clustering

M Soltanolkotabi EElhamifar EJ 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. educn Zhouchen Lin zhoulinmicrosoftcom Yong Yu yyuapexsjtueducn Shanghai Jiao Tong University NO 800 Dongchuan Road Min Hang District Shanghai China 200240 Microsoft Research Asia NO 49 Zhichun Road Hai Dian District Beijing China 100190 Abstract We edusg Department of Mechanical Engineering National University of Singapore Singapore 117576 Huan Xu mpexuhnusedusg Department of Mechanical Engineering National University of Singapore Singapore 117576 Abstract This paper considers the problem of su Derin Babacan University of Illinois at UrbanaChampaign Urbana IL 61801 USA dbabacangmailcom Shinichi Nakajima Nikon Corporation Tokyo 1408601 Japan nakajimasnikoncojp Minh N Do University of Illinois at UrbanaChampaign Urbana IL 61801 USA min k. -center clustering. Ilya Razenshteyn (MIT). Silvio . Lattanzi. (Google), Stefano . Leonardi. (. Sapienza. University of Rome) and . Vahab. . Mirrokni. (Google). k. -Center clustering. Given:. 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?. Brendan and Yifang . April . 21 . 2015. Pre-knowledge. We define a set A, and we find the element that minimizes the error. We can think of as a sample of . Where is the point in C closest to X. . Asymptotics. Yining Wang. , Jun . zhu. Carnegie Mellon University. Tsinghua University. 1. Subspace Clustering. 2. Subspace Clustering Applications. Motion Trajectories tracking. 1. 1 . (. Elhamifar. 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. Data segmentation and clustering. Given a set of points, separate them into multiple groups. Discriminative methods: learn boundary. 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. 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..