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...
Autores principales: | , |
---|---|
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 |
_version_ | 1780926864618946560 |
---|---|
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 |