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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Fu, Yun (Επιμελητής έκδοσης), Ma, Yunqian (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: New York, NY : Springer New York : Imprint: Springer, 2013.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
LEADER 02955nam a22004815i 4500
001 978-1-4614-4457-2
003 DE-He213
005 20151125161024.0
007 cr nn 008mamaa
008 121117s2013 xxu| s |||| 0|eng d
020 |a 9781461444572  |9 978-1-4614-4457-2 
024 7 |a 10.1007/978-1-4614-4457-2  |2 doi 
040 |d GrThAP 
050 4 |a TK1-9971 
072 7 |a TJK  |2 bicssc 
072 7 |a TEC041000  |2 bisacsh 
082 0 4 |a 621.382  |2 23 
245 1 0 |a Graph Embedding for Pattern Analysis  |h [electronic resource] /  |c edited by Yun Fu, Yunqian Ma. 
264 1 |a New York, NY :  |b Springer New York :  |b Imprint: Springer,  |c 2013. 
300 |a VIII, 260 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
505 0 |a 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. 
520 |a 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. 
650 0 |a Engineering. 
650 0 |a Artificial intelligence. 
650 0 |a Pattern recognition. 
650 0 |a Electrical engineering. 
650 1 4 |a Engineering. 
650 2 4 |a Communications Engineering, Networks. 
650 2 4 |a Pattern Recognition. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
650 2 4 |a Signal, Image and Speech Processing. 
700 1 |a Fu, Yun.  |e editor. 
700 1 |a Ma, Yunqian.  |e editor. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9781461444565 
856 4 0 |u http://dx.doi.org/10.1007/978-1-4614-4457-2  |z Full Text via HEAL-Link 
912 |a ZDB-2-ENG 
950 |a Engineering (Springer-11647)