'Laplacian matrix' presentation slideshows

Matrix Martingales in Randomized Numerical Linear Algebra

1.21k views • 119 slides

Privacy and Spectral Analysis on Social Network Randomization

Privacy and Spectral Analysis on Social Network Randomization. Xiaowei Ying, Leting Wu, Xintao Wu University of North Carolina at Charlotte. Framework. Background & Motivation Privacy in Randomized Graph Link privacy (3 method to quantify link privacy) Node privacy

1.23k views • 104 slides

Laplacian Matrices of Graphs: Algorithms and Applications

Laplacian Matrices of Graphs: Algorithms and Applications. Daniel A. Spielman. ICML, June 21, 2016. Outline. Laplacians Interpolation on graphs Spring networks Clustering Isotonic regression Sparsification Solving Laplacian Equations Best results The simplest algorithm.

1.25k views • 83 slides

Matrix Martingales in Randomized Numerical Linear Algebra

688 views • 68 slides

GPGPU in Medical Imaging Applications

GPGPU in Medical Imaging Applications. Kevin Gorczowski. Overview. Random Walks for Interactive Organ Segmentation in Two and Three Dimensions Interactive, GPU-Based Level Sets for 3D Brain Tumor Segmentation Image Registration by a Regularized Gradient Flow

705 views • 53 slides

Graph Partitioning

Graph Partitioning. Abhiram Ranade IIT Bombay. A load balancing problem. Input: undirected graph G. Vertices = processes. Edges = interprocess communication. integer k = number of available processors. Output: Assign processes to processors. , 4. A load balancing problem. Input:

671 views • 43 slides

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603 views • 41 slides

Partitional Algorithms to Detect Complex Clusters

Partitional Algorithms to Detect Complex Clusters. Kernel K-means K-means applied in Kernel space Spectral clustering Eigen subspace of the affinity matrix (Kernel matrix) Non-negative Matrix factorization (NMF)

462 views • 32 slides

Watermarking 3D Polygonal Meshes

Watermarking 3D Polygonal Meshes. ??????? ?????????? ???2002.4.24. Outline. 3D Mesh Object And VRML 3D Mesh Watermarking Attacks Spatial Domain Watermarks Frequency Domain Watermarks Future Works Reference . 3D Mesh Object And VRML. The 3D Mesh Object is a 3D Polygonal model.

480 views • 22 slides

Approximate computation and implicit regularization in large-scale data analysis

Approximate computation and implicit regularization in large-scale data analysis. Michael W. Mahoney Stanford University Sept 2011 (For more info, see: http://cs.stanford.edu/people/mmahoney). Motivating observation . Theory of NP-completeness is a very useful theory

324 views • 19 slides

1 University of Rouen, LITIS EA4108, Rouen, France 2 Centre Henri-Becquerel, Rouen, France

381 views • 19 slides

Graph Laplacian Regularization for Large-Scale Semidefinite Programming

Graph Laplacian Regularization for Large-Scale Semidefinite Programming. Kilian Weinberger et al. NIPS 2006 presented by Aggeliki Tsoli. Introduction. Problem discovery of low dimensional representations of high-dimensional data in many cases, local proximity measurements also available

377 views • 17 slides

CS 219 : Sparse Matrix Algorithms

CS 219 : Sparse Matrix Algorithms. John R. Gilbert ( gilbert@cs.ucsb.edu ) www.cs.ucsb.edu/~gilbert/ cs219. Systems of linear equations: Ax = b Eigenvalues and eigenvectors: Aw = ?w. Systems of linear equations: Ax = b. Alice is four years older than Bob.

287 views • 15 slides

Spectral Clustering

Spectral Clustering. Scatter plot of a 2D data set. K-means Clustering. Spectral Clustering. U. von Luxburg . A tutorial on spectral clustering. Technical report, Max Planck Institute for Biological Cybernetics, Germany, 2006. Conventional K-means Clustering.

1.3k views • 14 slides

Spectral Analysis based on the Adjacency Matrix of Network Data

Spectral Analysis based on the Adjacency Matrix of Network Data. Leting Wu Fall 2009. Mathematical Representation of Networks Data. Adjacency Matrix A If there is a link between vertexes i and vertex j, a ij =1(or positive number if it is a weighted adjacency matrix) otherwise 0

270 views • 14 slides