Overview
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.
Full Product Details
Author: Yun Fu ,
Yunqian Ma
Publisher: Springer-Verlag New York Inc.
Imprint: Springer-Verlag New York Inc.
Edition: 2013 ed.
Dimensions:
Width: 15.50cm
, Height: 1.40cm
, Length: 23.50cm
Weight: 4.102kg
ISBN: 9781489990624
ISBN 10: 1489990623
Pages: 260
Publication Date: 13 December 2014
Audience:
Professional and scholarly
,
Professional & Vocational
Format: Paperback
Publisher's Status: Active
Availability: Manufactured on demand
We will order this item for you from a manufactured on demand supplier.
Reviews
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)
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)
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)
Author Information
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.
