Graph embedding for pattern analysis:
<i>Graph Embedding for Pattern Analysis</i> 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, linea...
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| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
New York, NY [u.a.]
Springer
2013
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| Schlagwörter: | |
| Links: | https://doi.org/10.1007/978-1-4614-4457-2 https://doi.org/10.1007/978-1-4614-4457-2 https://doi.org/10.1007/978-1-4614-4457-2 https://doi.org/10.1007/978-1-4614-4457-2 https://doi.org/10.1007/978-1-4614-4457-2 https://doi.org/10.1007/978-1-4614-4457-2 https://doi.org/10.1007/978-1-4614-4457-2 https://doi.org/10.1007/978-1-4614-4457-2 https://doi.org/10.1007/978-1-4614-4457-2 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025685326&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025685326&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| Zusammenfassung: | <i>Graph Embedding for Pattern Analysis</i> 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 |
| Beschreibung: | Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces -- Feature Grouping and Selection over an Undirected Graph -- Median Graph Computation by Means of Graph Embedding into Vector Spaces -- Patch Alignment for Graph Embedding -- Feature Subspace Transformations for Enhancing K-Means Clustering -- Learning with ℓ1-Graph for High Dimensional Data Analysis -- Graph-Embedding Discriminant Analysis on Riemannian Manifolds for Visual Recognition -- A Flexible and Effective Linearization Method for Subspace Learning -- A Multi-Graph Spectral Approach for Mining Multi-Source Anomalies -- <p>Graph Embedding for Speaker Recognition.</p> |
| Umfang: | 1 Online-Ressource (VIII, 260 p. 91 illus., 63 illus. in color) |
| ISBN: | 9781461444572 |
| DOI: | 10.1007/978-1-4614-4457-2 |
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Datensatz im Suchindex
| _version_ | 1819366313675456512 |
|---|---|
| adam_text | GRAPH EMBEDDING FOR PATTERN ANALYSIS
/
: 2013
TABLE OF CONTENTS / INHALTSVERZEICHNIS
MULTILEVEL ANALYSIS OF ATTRIBUTED GRAPHS FOR EXPLICIT GRAPH EMBEDDING IN
VECTOR SPACES
FEATURE GROUPING AND SELECTION OVER AN UNDIRECTED GRAPH
MEDIAN GRAPH COMPUTATION BY MEANS OF GRAPH EMBEDDING INTO VECTOR SPACES
PATCH ALIGNMENT FOR GRAPH EMBEDDING
FEATURE SUBSPACE TRANSFORMATIONS FOR ENHANCING K-MEANS CLUSTERING
LEARNING WITH ℓ1-GRAPH FOR HIGH DIMENSIONAL DATA ANALYSIS
GRAPH-EMBEDDING DISCRIMINANT ANALYSIS ON RIEMANNIAN MANIFOLDS FOR VISUAL
RECOGNITION
A FLEXIBLE AND EFFECTIVE LINEARIZATION METHOD FOR SUBSPACE LEARNING
A MULTI-GRAPH SPECTRAL APPROACH FOR MINING MULTI-SOURCE ANOMALIES
GRAPH EMBEDDING FOR SPEAKER RECOGNITION
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
GRAPH EMBEDDING FOR PATTERN ANALYSIS
/
: 2013
ABSTRACT / INHALTSTEXT
GRAPH EMBEDDING FOR PATTERNANALYSIS 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
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
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| id | DE-604.BV040704869 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T16:23:48Z |
| institution | BVB |
| isbn | 9781461444572 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025685326 |
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| physical | 1 Online-Ressource (VIII, 260 p. 91 illus., 63 illus. in color) |
| psigel | ZDB-2-ENG |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Springer |
| record_format | marc |
| spellingShingle | Graph embedding for pattern analysis Ingenieurwissenschaften Künstliche Intelligenz Engineering Artificial intelligence Optical pattern recognition Telecommunication |
| title | Graph embedding for pattern analysis |
| title_auth | Graph embedding for pattern analysis |
| title_exact_search | Graph embedding for pattern analysis |
| title_full | Graph embedding for pattern analysis Yun Fu ..., eds. |
| title_fullStr | Graph embedding for pattern analysis Yun Fu ..., eds. |
| title_full_unstemmed | Graph embedding for pattern analysis Yun Fu ..., eds. |
| title_short | Graph embedding for pattern analysis |
| title_sort | graph embedding for pattern analysis |
| topic | Ingenieurwissenschaften Künstliche Intelligenz Engineering Artificial intelligence Optical pattern recognition Telecommunication |
| topic_facet | Ingenieurwissenschaften Künstliche Intelligenz Engineering Artificial intelligence Optical pattern recognition Telecommunication |
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