PPT-Network Lasso: Clustering and Optimization in

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. 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.. Justin Underwood. D. esigned . to select areas of the current layer or image based on color similarity. .. Good for selecting sharp images. . Magic Wand. From the image menu bar Tools → Selection Tools → Fuzzy . s. tructured signals: . Precise performance analysis. Christos Thrampoulidis. Joint . ITA Workshop, La Jolla, CA. February 3, 2016. Let’s start “simple”…. Given . y . and . A. can you find . 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 . Gonzalo . Mateos. , Juan A. Bazerque, and . Georgios. B. Giannakis . Acknowledgement:. . NSF grants . CCF-0830480, 1016605 and . ECCS-0824007. January 6, 2011. Distributed sparse estimation. 2. Data acquired by . 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.. 23. rd. . September 2015. Brian Booden. Agenda. Introduction. Motivation. D3.js – Finding . e. xamples and tutorials. Find some collaborators. Make it happen. D3 Conversion and selections. Colour and Area Binning. 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”. 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. 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. 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. Randomization tests. Cluster Validity . All clustering algorithms provided with a set of points output a clustering. How . to evaluate the “goodness” of the resulting clusters?. Tricky because . What is clustering?. Grouping set of documents into subsets or clusters.. The Goal of clustering algorithm is:. To create clusters that are coherent internally, but clearly different from each other.