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Engineering - Signals & Communication | Graph Embedding for Pattern Analysis

Graph Embedding for Pattern Analysis

Fu, Yun, Ma, Yunqian (Eds.)

2013, VIII, 260 p.

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  • 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
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.

Content Level » Research

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

Related subjects » Artificial Intelligence - Image Processing - Signals & Communication

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