Graph Embedding for Pattern Analysis
1st Edition
Published on 19. November 2012
VIII, 260 pages
978-1-4614-4457-2 (ISBN)
Description
Graph Embedding for Pattern Recognition 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, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.
Reviews / Votes
From the reviews:
"The papers in this collection apply the methods elaborated in classical and algebraic graph theory to analyze patterns in various contexts. . the book will be easy for a researcher well versed in the theoretical fundamentals of the presented methods. . the editors have been able to structure the contents in an effective and interesting way. Therefore, I can recommend this volume as a useful reference for specialists in the field." (Piotr Cholda, Computing Reviews, November, 2013)
More details
Other editions
Additional editions
Yun Fu | Yunqian Ma
Graph Embedding for Pattern Analysis
Book
11/2012
Springer
€106.99
Shipment within 15-20 days
Persons
Dr. Yun Fu is a professor at the State University of New York at Buffalo
Dr. Yunqian Ma is a senior principal research scientist of Honeywell Labs at the Honeywell International Inc.
Dr. Yunqian Ma is a senior principal research scientist of Honeywell Labs at the Honeywell International Inc.
Content
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 l1-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.
