PPT-Network Lasso: Clustering and Optimization in

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Large Graphs David Hallac Jure Leskovec Stephen Boyd Stanford University Presented by Yu Zhao What is this paper about Lasso problem The lasso solution is

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Network Lasso: Clustering and Optimization in: Transcript

Large Graphs David Hallac Jure Leskovec Stephen Boyd Stanford University Presented by Yu Zhao What is this paper about Lasso problem The lasso solution is unique when rankX p because the criterion is strictly convex. Adapted from Chapter 3. Of. Lei Tang and . Huan. Liu’s . Book. Slides prepared by . Qiang. Yang, . UST, . HongKong. 1. Chapter 3, Community Detection and Mining in Social Media. Lei Tang and Huan Liu, Morgan & Claypool, September, 2010. . J. Friedman, T. Hastie, R. . Tibshirani. Biostatistics, 2008. Presented by . Minhua. Chen. 1. Motivation. Mathematical Model. Mathematical Tools. Graphical LASSO. Related papers. 2. Outline. Motivation. M. agic Wand. By: Alex Ramirez. What it is?. The Lasso tool allows you to draw a free form shape to create a selection. .. T. he . Magic Wand . tool looks . for differences in color and contrast (pixel differences) depending upon various parameters you set.. and. Distributed Network Algorithms. Rajmohan Rajaraman. Northeastern University, Boston. May 2012. Chennai Network Optimization Workshop. AND and DNA. 1. Overview of the 4 Sessions. Random walks. Percolation processes. Statistics for High-Dimensional Data (. Buhlmann. & van de Geer). Lasso. Proposed by . Tibshirani. (1996). Least Absolute Shrinkage and Selection Operator. Why we still use it. Accurate in prediction and variable selection (under certain assumptions) and computationally feasible. Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. Computer Sciences 838. https://compnetbiocourse.discovery.wisc.edu. Nov 3. rd. , Nov 10. Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. Computer Sciences 838. https://compnetbiocourse.discovery.wisc.edu. Nov 1. st. 2016. Some material is adapted from lectures from Introduction to Bioinformatics. Yining Wang. , Yu-Xiang Wang, . Aarti. Singh. Machine Learning Department. Carnegie . mellon. university. 1. Subspace Clustering. 2. Subspace Clustering Applications. Motion Trajectories tracking. 1. for . Adaptive . C. ircuit . D. esign. Ang. Lu, . Hao. He, and Jiang Hu. Introduction. Proposed Techniques. Experiment Result. Conclusion. Overview. 2. Design Challenges. Process Variations. Device Aging. What it is?. The Lasso tool allows you to draw a free form shape to create a selection. .. T. he . Magic Wand . tool looks . for differences in color and contrast (pixel differences) depending upon various parameters you set.. and Cluster Analysis. Dissertation Defense. Nan Li. Committee. : Dr. . Longin. Jan . Latecki. (Advisor). Dr. . Haibin. Ling. Dr. Slobodan . Vucetic. Saket. . vishwasrao. Swapna . thorve. May 3, 2016. Spring 2016. CS 5604 Information storage and retrieval. Instructor : Dr. Edward Fox. . Virginia Polytechnic Institute and State University, Blacksburg, VA 24061. J. Friedman, T. Hastie, R. . Tibshirani. Biostatistics, 2008. Presented by . Minhua. Chen. 1. Motivation. Mathematical Model. Mathematical Tools. Graphical LASSO. Related papers. 2. Outline. Motivation. Non-convex optimization. All loss-functions that are not convex: not very informative.. Global optimality: too strong. Weaker notions of optimality?. What is a saddle point?. Different kinds of critical/stationary points.

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