001454878 000__ 03648cam\a2200529\i\4500
001454878 001__ 1454878
001454878 003__ OCoLC
001454878 005__ 20230314003232.0
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001454878 007__ cr\cn\nnnunnun
001454878 008__ 230227s2023\\\\sz\a\\\\ob\\\\000\0\eng\d
001454878 019__ $$a1371099179$$a1371143206
001454878 020__ $$a9783031224386$$q(electronic bk.)
001454878 020__ $$a3031224388$$q(electronic bk.)
001454878 020__ $$z303122437X
001454878 020__ $$z9783031224379
001454878 0247_ $$a10.1007/978-3-031-22438-6$$2doi
001454878 035__ $$aSP(OCoLC)1371239726
001454878 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP
001454878 049__ $$aISEA
001454878 050_4 $$aTK5102.9
001454878 08204 $$a621.38220151954$$223/eng/20230227
001454878 1001_ $$aCheng, Lei,$$eauthor.
001454878 24510 $$aBayesian tensor decomposition for signal processing and machine learning :$$bmodeling, tuning-free algorithms and applications /$$cLei Cheng, Zhongtao Chen, Yik-Chung Wu.
001454878 264_1 $$aCham, Switzerland :$$bSpringer,$$c2023.
001454878 300__ $$a1 online resource (180 pages) :$$billustrations (black and white, and colour).
001454878 336__ $$atext$$btxt$$2rdacontent
001454878 337__ $$acomputer$$bc$$2rdamedia
001454878 338__ $$aonline resource$$bcr$$2rdacarrier
001454878 504__ $$aIncludes bibliographical references.
001454878 5050_ $$aTensor decomposition: Basics, algorithms, and recent advances -- Bayesian learning for sparsity-aware modeling -- Bayesian tensor CPD: Modeling and inference -- Bayesian tensor CPD: Performance and real-world applications -- When stochastic optimization meets VI: Scaling Bayesian CPD to massive data -- Bayesian tensor CPD with nonnegative factors -- Complex-valued CPD, orthogonality constraint and beyond Gaussian noises -- Handling missing value: A case study in direction-of-arrival estimation -- From CPD to other tensor decompositions.
001454878 506__ $$aAccess limited to authorized users.
001454878 520__ $$aThis book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, including blind source separation; social network mining; image and video processing; array signal processing; and, wireless communications. The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed. Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods.
001454878 588__ $$aDescription based on print version record.
001454878 650_0 $$aSignal processing$$xStatistical methods.
001454878 650_0 $$aMachine learning$$xStatistical methods.
001454878 650_0 $$aBayesian statistical decision theory.
001454878 655_0 $$aElectronic books.
001454878 7001_ $$aChen, Zhongtao,$$eauthor.
001454878 7001_ $$aWu, Yik-Chung,$$eauthor.
001454878 77608 $$iPrint version:$$aCHENG, LEI. CHEN, ZHONGTAO. WU, YIK-CHUNG.$$tBAYESIAN TENSOR DECOMPOSITION FOR SIGNAL PROCESSING AND MACHINE LEARNING.$$d[Place of publication not identified] : SPRINGER INTERNATIONAL PU, 2023$$z303122437X$$w(OCoLC)1349448636
001454878 852__ $$bebk
001454878 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-22438-6$$zOnline Access$$91397441.1
001454878 909CO $$ooai:library.usi.edu:1454878$$pGLOBAL_SET
001454878 980__ $$aBIB
001454878 980__ $$aEBOOK
001454878 982__ $$aEbook
001454878 983__ $$aOnline
001454878 994__ $$a92$$bISE