PDF-Noisy Sparse Subspace Clustering YuXiang Wang yuxiangwangnus

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edusg Department of Mechanical Engineering National University of Singapore Singapore 117576 Huan Xu mpexuhnusedusg Department of Mechanical Engineering National

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Noisy Sparse Subspace Clustering YuXiang Wang yuxiangwangnus: Transcript

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. cmuedu Huan Xu Dept of Mech Engineering National Univ of Singapore Singapore 117576 mpexuhnusedusg Chenlei Leng Department of Statistics University of Warwick Coventry CV4 7AL UK CLengwarwickacuk Abstract Sparse Subspace Clustering SSC and LowRank Re Chun Lam Chan. , Pak . Hou. . Che. and . Sidharth. . Jaggi. The Chinese University of Hong Kong. Venkatesh. . Saligrama. Boston University. Non-adaptive probabilistic group testing with noisy measurements: Near-optimal bounds with efficient algorithms. 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 . Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . 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. Iterative Methods. Erin Carson. UC Berkeley Parallel Computing Lab. BeBop. Group. Discovery 2015: HPC and Cloud Computing. Workshop, June 2011. President . Obama. cites Communication Avoiding algorithms in the FY 2012 Department of Energy Budget Request to Congress:. Yining Wang. , Yu-Xiang Wang, . Aarti. Singh. Machine Learning Department. Carnegie . mellon. university. 1. Subspace Clustering. 2. Subspace Clustering Applications. Motion Trajectories tracking. 1. 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 ∈ . 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. Venkat. . Guruswami. , Nicolas Resch and . Chaoping. Xing. Algebraic . Pseudorandomness. Traditional pseudorandom objects (e.g., . expander graphs. , . randomness extractors. , . pseudorandom generators. What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. Spikes in trigger rate. Periodic:. With B ON in 2008 . Without B on during MWGR18 . Sporadic . MWGR 19. Strip noise profile. 6 may . 22 April. REASON: HV problem in RB1 out sect 12. Noisy topology. 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.. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A . tree-like . diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering.

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