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020			_a9783030743864
024	7		_a10.1007/978-3-030-74386-4 _2doi
040			_aTR-AnTOB _beng _cTR-AnTOB _erda
041			_aeng
050		4	_aTA347.T4
072		7	_aTJFC _2bicssc
072		7	_aTEC008010 _2bisacsh
072		7	_aTJFC _2thema
090			_aTA347.T4EBK
100	1		_aLiu, Yipeng. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aTensor Computation for Data Analysis _h[electronic resource] / _cby Yipeng Liu, Jiani Liu, Zhen Long, Ce Zhu.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_a1- Tensor Computation -- 2-Tensor Decomposition -- 3-Tensor Dictionary Learning -- 4-Low Rank Tensor Recovery -- 5-Coupled Tensor for Data Analysis -- 6-Robust Principal Tensor Component Analysis -- 7-Tensor Regression -- 8-Statistical Tensor Classification -- 9-Tensor Subspace Cluster -- 10-Tensor Decomposition in Deep Networks -- 11-Deep Networks for Tensor Approximation -- 12-Tensor-based Gaussian Graphical Model -- 13-Tensor Sketch. .
520			_aTensor is a natural representation for multi-dimensional data, and tensor computation can avoid possible multi-linear data structure loss in classical matrix computation-based data analysis. This book is intended to provide non-specialists an overall understanding of tensor computation and its applications in data analysis, and benefits researchers, engineers, and students with theoretical, computational, technical and experimental details. It presents a systematic and up-to-date overview of tensor decompositions from the engineer's point of view, and comprehensive coverage of tensor computation based data analysis techniques. In addition, some practical examples in machine learning, signal processing, data mining, computer vision, remote sensing, and biomedical engineering are also presented for easy understanding and implementation. These data analysis techniques may be further applied in other applications on neuroscience, communication, psychometrics, chemometrics, biometrics, quantum physics, quantum chemistry, etc. The discussion begins with basic coverage of notations, preliminary operations in tensor computations, main tensor decompositions and their properties. Based on them, a series of tensor-based data analysis techniques are presented as the tensor extensions of their classical matrix counterparts, including tensor dictionary learning, low rank tensor recovery, tensor completion, coupled tensor analysis, robust principal tensor component analysis, tensor regression, logistical tensor regression, support tensor machine, multilinear discriminate analysis, tensor subspace clustering, tensor-based deep learning, tensor graphical model and tensor sketch. The discussion also includes a number of typical applications with experimental results, such as image reconstruction, image enhancement, data fusion, signal recovery, recommendation system, knowledge graph acquisition, traffic flow prediction, link prediction, environmental prediction, weather forecasting, background extraction, human pose estimation, cognitive state classification from fMRI, infrared small target detection, heterogeneous information networks clustering, multi-view image clustering, and deep neural network compression.
650		0	_aElectronic circuits.
650		0	_aSignal processing.
650		0	_aCooperating objects (Computer systems).
650	1	4	_aElectronic Circuits and Systems.
650	2	4	_aDigital and Analog Signal Processing.
650	2	4	_aCyber-Physical Systems.
653		0	_aCalculus of tensors
700	1		_aLiu, Jiani. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aLong, Zhen. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aZhu, Ce. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-3-030-74386-4 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK