'Graph embedding' presentation slideshows

Adaptive Graph Construction and Dimensionality Reduction

Adaptive Graph Construction and Dimensionality Reduction. Songcan Chen, Lishan Qiao, Limei Zhang http://parnec.nuaa.edu.cn/ {s.chen, qiaolishan, zhanglimei}@nuaa.edu.cn. 2009. 11. 06. Outline. Why to construct graph? Typical graph construction Review & Challenges Our works

633 views • 62 slides

Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

Graph Embedding and Extensions: A General Framework for Dimensionality Reduction. IEEE TRANSSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Shuicheng Yan, Dong Xu, Benyu Zhang, Hong-Jiang Zhang, Qiang Yang, Stephen Lin Presented by meconin. Outline. Introduction Graph Embedding (GE)

762 views • 54 slides

Lecture 13: SVM Again

Lecture 13: SVM Again. Machine Learning Queens College. Today. Completion of Support Vector Machines Project Description and Topics. Support Vectors. Support Vectors are those input points (vectors) closest to the decision boundary 1. They are vectors

395 views • 26 slides

Element Rearrangement for Tensor based Subspace Learning

Element Rearrangement for Tensor based Subspace Learning. Dong XU School of Computer Engineering Nanyang Technological University. What is Tensor?. Tensors are arrays of numbers which transform in certain ways under coordinate transformations. Vector. Matrix. 3 rd -order Tensor.

496 views • 24 slides

Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

Graph Embedding and Extensions: A General Framework for Dimensionality Reduction. Keywords: Dimensionality reduction, manifold learning, subspace learning, graph embedding framework. 1.Introduction. Techniques for dimensionality reduction Linear: PCA/LDA/LPP...

220 views • 21 slides

Geopolitics of routing, What RIPE tells us of geography and power: a methodological approach

189 views • 18 slides