Graph Embedding for Pattern Analysis

Beschreibung

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, 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.
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.
Covers theoretical analysis and real-world applications for graph embedding Examines subspace analysis with L1 graph Describes graph-based inference on Riemannian manifolds for visual analysis Includes supplementary material: sn.pub/extras

Autor*in

Yun Fu

Themen in »Graph Embedding for Pattern Analysis«

Computer Vision Dimensionality Reduction Discriminant Analysis Graph Embedding Hypergraph Machine Learning Manifold Learning Pattern Recognition Subspace Learning

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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)

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