'Reconstruction error' diaporamas de présentation

CSE 446 Dimensionality Reduction and PCA Winter 2012

CSE 446 Dimensionality Reduction and PCA Winter 2012. Slides adapted from Carlos Guestrin & Luke Zettlemoyer. Machine Learning. Supervised Learning. Unsupervised Learning. Fri K- means & A gglomerative C lustering Mon Expectation Maximization (EM)

557 views • 41 slides

Image Compression

868 views • 40 slides

Principal Component Analysis

Principal Component Analysis. Jieping Ye Department of Computer Science and Engineering Arizona State University http://www.public.asu.edu/~jye02. Outline of lecture. What is feature reduction? Why feature reduction? Feature reduction algorithms Principal Component Analysis (PCA)

831 views • 34 slides

CONTACT-LESS 3D COORDINATE MEASUREMENT SYSTEM BY LASER SCANNING AND IMAGE RECONSTRUCTION

390 views • 25 slides

In the name of god

In the name of god. Autoencoders Mostafa Heidarpour. Autoencoders. An auto-encoder is an artificial neural network used for learning efficient codings The aim of an auto-encoder is to learn a compressed representation (encoding) for a set of data

311 views • 25 slides

Face detection and recognition

398 views • 23 slides

Out-of-Sample Extension and Reconstruction on Manifolds

Out-of-Sample Extension and Reconstruction on Manifolds. Bhuwan Dhingra Final Year (Dual Degree) Dept of Electrical Engg. Introduction. An m - dimensional manifold is a topological space which is locally homeomorphic to the m -dimensional E uclidean space

325 views • 23 slides

Matthijs van Eede University of Toronto August 22nd, 2006

Matthijs van Eede University of Toronto August 22nd, 2006 Joint work with Diego Macrini, Alex Telea, Cristian Sminchisescu, and Sven Dickinson.

342 views • 21 slides

Performance Analysis of Three Likelihood Measures for Color Image Processing

Performance Analysis of Three Likelihood Measures for Color Image Processing. Outline. Introduction Image Segmentation, Color Image Segmentation, Fuzzy Membership, What we have done. Method Likelihood Measure, Homogeneity Criteria, Fuzzy Membership, PCA Everywhere, Different Color spaces.

339 views • 20 slides

Adaptive Data Collection in Environmental Sensor Networks

Adaptive Data Collection in Environmental Sensor Networks. Jayant Gupchup gupchup@jhu.edu. Cub Hill Network. Question. Downloading data from everyone is expensive Radio usage consumes power Most of the time nothing interesting is happening Data is strongly correlated. Proposal.

326 views • 19 slides

M.I.Pujol, G.Dibarboure, G. Larnicol (CLS) P.Y.Le Traon, P.Klein (IFREMER),

New diagnostics to assess the impact of satellite constellation for (sub)mesoscale applications Complementarity between SWOT and a large constellation of pulse-limited altimeters. M.I.Pujol, G.Dibarboure, G. Larnicol (CLS) P.Y.Le Traon, P.Klein (IFREMER),. Introduction.

269 views • 18 slides

Distributed Feature-Specific Imaging

Distributed Feature-Specific Imaging. Jun Ke 1 , Premchandra Shankar 1 , and Mark A. Neifeld 1,2. 1 Department of Electrical and Computer Engineering, 2 College of Optical Sciences University of Arizona. Computational Optical Sensing and Imaging (COSI) 2007. Outline. Motivation

258 views • 13 slides

An Adaptive Data Forwarding Scheme for Energy Efficiency in Wireless Sensor Networks

An Adaptive Data Forwarding Scheme for Energy Efficiency in Wireless Sensor Networks. Christos Anagnostopoulos Theodoros Anagnostopoulos Stathes Hadjiefthymiades Dept. of Informatics and Telecommunications, National and Kapodistrian University of Athens,

243 views • 10 slides