Graph Embedding for Pattern 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, gr...

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Detalles Bibliográficos
Autores principales: Fu, Yun, Ma, Yunqian
Lenguaje:eng
Publicado: Springer 2013
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-1-4614-4457-2
http://cds.cern.ch/record/1500223
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author Fu, Yun
Ma, Yunqian
author_facet Fu, Yun
Ma, Yunqian
author_sort Fu, Yun
collection CERN
description 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.
id cern-1500223
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2013
publisher Springer
record_format invenio
spelling cern-15002232021-04-22T00:02:19Zdoi:10.1007/978-1-4614-4457-2http://cds.cern.ch/record/1500223engFu, YunMa, YunqianGraph Embedding for Pattern AnalysisEngineeringGraph 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.Springeroai:cds.cern.ch:15002232013
spellingShingle Engineering
Fu, Yun
Ma, Yunqian
Graph Embedding for Pattern Analysis
title Graph Embedding for Pattern Analysis
title_full Graph Embedding for Pattern Analysis
title_fullStr Graph Embedding for Pattern Analysis
title_full_unstemmed Graph Embedding for Pattern Analysis
title_short Graph Embedding for Pattern Analysis
title_sort graph embedding for pattern analysis
topic Engineering
url https://dx.doi.org/10.1007/978-1-4614-4457-2
http://cds.cern.ch/record/1500223
work_keys_str_mv AT fuyun graphembeddingforpatternanalysis
AT mayunqian graphembeddingforpatternanalysis