PPT-Noisy Sparse Subspace Clustering with dimension reduction

Author : alexa-scheidler | Published Date : 2017-12-25

Yining Wang YuXiang Wang Aarti Singh Machine Learning Department Carnegie mellon university 1 Subspace Clustering 2 Subspace Clustering Applications Motion Trajectories

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Noisy Sparse Subspace Clustering with dimension reduction: Transcript

Yining Wang YuXiang Wang Aarti Singh Machine Learning Department Carnegie mellon university 1 Subspace Clustering 2 Subspace Clustering Applications Motion Trajectories tracking 1. 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 of . L. p. Yair. . Bartal. Lee-Ad Gottlieb. Ofer. Neiman. Embedding and Distortion. L. p. spaces: . L. p. k. is the metric space . Let (. X,d. ) be a finite metric space. A map f:X. →. . L. p. via Interpolation. Seung-Hee. . Bae. , . Jong. Youl Choi, Judy . Qiu. , and Geoffrey Fox. School of Informatics and Computing. Pervasive Technology Institute. Indiana University. S. A. L. S. A. project. 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. 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. 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.. 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. Venkat. . Guruswami. , Nicolas Resch and . Chaoping. Xing. Algebraic . Pseudorandomness. Traditional pseudorandom objects (e.g., . expander graphs. , . randomness extractors. , . pseudorandom generators. l. p. (1<p<2), with applications. Yair. . Bartal. . Lee-Ad Gottlieb Hebrew U. Ariel University. Introduction. Fundamental result in dimension reduction: Johnson-. Lindenstrauss. Lemma (JL-84) for Euclidean space.. 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.. Metric . Embeddings. COMS E6998-9. . F15. Administrivia. , Plan. PS2:. Pick up after class. 120->144 auto extension. Plan:. Least Squares Regression (finish). Metric . Embeddings. “reductions for distances”.

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