'Geodesic distances' diaporamas de présentation

Similarities, Distances and Manifold Learning

Similarities, Distances and Manifold Learning. Prof. Richard C. Wilson Dept. of Computer Science University of York. Background. Typically objects are characterised by features Face images SIFT features Object spectra ... If we measure n features ? n -dimensional space

1k views • 80 slides

Nonlinear Dimensionality Reduction

Nonlinear Dimensionality Reduction. Donovan Parks. Overview. Direct visualization vs. dimensionality reduction Nonlinear dimensionality reduction techniques: ISOMAP, LLE, Charting A fun example that uses non-metric, replicated MDS. Direct visualization. Visualize all dimensions.

685 views • 38 slides

A Global Geometric Framework for Nonlinear Dimensionality Reduction

A Global Geometric Framework for Nonlinear Dimensionality Reduction. Joshua B. Tenenbaum, Vin de Silva, John C. Langford Presented by Napat Triroj. Dimensionality Reduction. Objective: to find a small number of features that represent a large number of observed dimensions.

148 views • 13 slides

250 views • 11 slides

Face Recognition using Tensor Analysis

Face Recognition using Tensor Analysis. Presented by Prahlad R Enuganti. Face Recognition. Why is it necessary? Human Computer Interaction Authentication Surveillance Problems include change in Illumination Expression Pose Aging. Existing Techniques.

290 views • 9 slides