'Largest eigenvalue' diaporamas de présentation

Propagation on Large Networks

1.08k views • 101 slides

PCA explained within the context of Face Recognition

814 views • 67 slides

Robust PCA in Stata

Robust PCA in Stata. Vincenzo Verardi (vverardi@fundp.ac.be) FUNDP (Namur) and ULB (Brussels), Belgium FNRS Associate Researcher. Principal component analysis. PCA, transforms a set of correlated variables into a smaller set of uncorrelated variables (principal components).

746 views • 53 slides

Entropy Waves, The Zigzag Graph Product, and New Constant-Degree Expanders

Entropy Waves, The Zigzag Graph Product, and New Constant-Degree Expanders. Omer Reingold Salil Vadhan Avi Wigderson Lecturer: Oded Levy. Introduction. Most random constant degree graphs are good expanders. Most applications need explicit constructions.

612 views • 49 slides

Computational issues in nanotechnology and stochastic computing

Computational issues in nanotechnology and stochastic computing. Ashok Srinivasan Department of Computer Science Florida State University. Motivation. Research areas Parallel algorithms Scientific computing Discrete algorithms Applications. Motivation ... 2.

519 views • 39 slides

Computer Vision

408 views • 39 slides

396 views • 28 slides

Introduction to PageRank Algorithm and Programming Assignment 1

Introduction to PageRank Algorithm and Programming Assignment 1. CSC4170 Web Intelligence and Social Computing Tutorial 4 Tutor: Tom Chao Zhou Email: czhou@cse.cuhk.edu.hk. Outline. Background Markov Chains PageRank Computation Exercise on PageRank Example of Programming Assignment QA.

495 views • 14 slides

Introduction to PageRank Algorithm and Programming Assignment 1

Introduction to PageRank Algorithm and Programming Assignment 1. CSC4170 Web Intelligence and Social Computing Tutorial 4 Tutor: Tom Chao Zhou Email: czhou@cse.cuhk.edu.hk. Outline. Background Markov Chains PageRank Computation Exercise on PageRank Example of Programming Assignment QA.

308 views • 14 slides

Recognition by Appearance

Recognition by Appearance. Appearance-based recognition is a competing paradigm to features and alignment. No features are extracted! Images are represented by basis functions (eigenvectors) and their coefficients . Matching is performed on this compressed image representation.

123 views • 12 slides

Principal Components Analysis (PCA) 273A Intro Machine Learning

Principal Components Analysis (PCA) 273A Intro Machine Learning. Principal Components Analysis. We search for those directions in space that have the highest variance. We then project the data onto the subspace of highest variance.

351 views • 5 slides

Projected Hessian and quasi-Newton FWI

Yong Ma. Projected Hessian and quasi-Newton FWI. Sparse model space for projected Hessian. Eigenvector (largest eigenvalue ). Eigenvector (smallest eigenvalue ). Quasi-Newton FWI.

165 views • 1 slides