000			02960nam a22004935i 4500
001			978-1-4614-4457-2
003			DE-He213
005			20200421112038.0
007			cr nn 008mamaa
008			121117s2013 xxu| s |||| 0|eng d
020			_a9781461444572 _9978-1-4614-4457-2
024	7		_a10.1007/978-1-4614-4457-2 _2doi
050		4	_aTK1-9971
072		7	_aTJK _2bicssc
072		7	_aTEC041000 _2bisacsh
082	0	4	_a621.382 _223
245	1	0	_aGraph Embedding for Pattern Analysis _h[electronic resource] / _cedited by Yun Fu, Yunqian Ma.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013.
300			_aVIII, 260 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_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.
520			_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.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650		0	_aPattern recognition.
650		0	_aElectrical engineering.
650	1	4	_aEngineering.
650	2	4	_aCommunications Engineering, Networks.
650	2	4	_aPattern Recognition.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aSignal, Image and Speech Processing.
700	1		_aFu, Yun. _eeditor.
700	1		_aMa, Yunqian. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781461444565
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4614-4457-2
912			_aZDB-2-ENG
942			_cEBK
999			_c56454 _d56454