Graph Embedding for Pattern Analysis

• Corporate Author: SpringerLink (Online service)
• Other Authors: Fu, Yun. (Editor), Ma, Yunqian. (Editor)
• Format: Electronic
• Language: English
• Published: New York, NY : Springer New York : Imprint: Springer, 2013.
• Subjects: Engineering.; Artificial intelligence.; Optical pattern recognition.; Telecommunication.; Communications Engineering, Networks.; Pattern Recognition.; Artificial Intelligence (incl. Robotics).; Signal, Image and Speech Processing.
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-4614-4457-2

Graph Embedding for Pattern�Analysis covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, gr...

Table of Contents:

• Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces
• Feature Grouping and Selection over an Undirected Graph
• Median Graph Computation by Means of Graph Embedding into Vector Spaces
• Patch Alignment for Graph Embedding
• Feature Subspace Transformations for Enhancing K-Means Clustering
• Learning with 1-Graph for High Dimensional Data Analysis
• Graph-Embedding Discriminant Analysis on Riemannian Manifolds for Visual Recognition
• A Flexible and Effective Linearization Method for Subspace Learning
• A Multi-Graph Spectral Approach for Mining Multi-Source Anomalies
• Graph Embedding for Speaker Recognition.