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020			_a9783030743864
024	7		_a10.1007/978-3-030-74386-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA347.T4 _b2022 EB
100	1		_aLiu, Yipeng _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687435
245	1	0	_aTensor Computation for Data Analysis _cby Yipeng Liu, Jiani Liu, Zhen Long, Ce Zhu
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XX, 338 páginas) _b132 ilustraciones, 119 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
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.
988			_aSpringer_Engineering_2022
650		7	_2embne _9139229 _aCálculo tensorial
700	1		_9687436 _aLiu, Jiani _eautor
700	1		_9687437 _aLong, Zhen _eautor
700	1		_9687438 _aZhu, Ce, _d1969- _eautor
776	0	8	_iPrinted edition: _z9783030743857
776	0	8	_iPrinted edition: _z9783030743871
776	0	8	_iPrinted edition: _z9783030743888
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-74386-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eIG _zSI