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024 7 $a10.1007/978-1-4614-4457-2
035   $a(CKB)2670000000312768
035   $a(EBL)1081845
035   $a(OCoLC)823729120
035   $a(SSID)ssj0000811031
035   $a(PQKBManifestationID)11456349
035   $a(PQKBTitleCode)TC0000811031
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035   $a(DE-He213)978-1-4614-4457-2
035   $a(MiAaPQ)EBC1081845
035   $a(PPN)16830029X
035   $a(EXLCZ)992670000000312768
100   $a20121117d2013      u|             0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aGraph Embedding for Pattern Analysis$b[electronic resource] /$fedited by Yun Fu, Yunqian Ma
205   $a1st ed. 2013.
210  1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2013.
215   $a1 online resource (263 p.)
300   $aDescription based upon print version of record.
311   $a1-4899-9062-3 
311   $a1-4614-4456-X 
320   $aIncludes bibliographical references.
327   $aMultilevel 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 ?1-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.
330   $aGraph 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.
606   $aElectrical engineering
606   $aPattern recognition
606   $aArtificial intelligence
606   $aSignal processing
606   $aImage processing
606   $aSpeech processing systems
606   $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035
606   $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051
615  0$aElectrical engineering.
615  0$aPattern recognition.
615  0$aArtificial intelligence.
615  0$aSignal processing.
615  0$aImage processing.
615  0$aSpeech processing systems.
615 14$aCommunications Engineering, Networks.
615 24$aPattern Recognition.
615 24$aArtificial Intelligence.
615 24$aSignal, Image and Speech Processing.
676   $a006.4
702   $aFu$b Yun$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aMa$b Yunqian$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a9910437903303321
996   $aGraph Embedding for Pattern Analysis$92502775
997   $aUNINA