LEADER 03243nam 2200493 450 001 9910672446803321 005 20230523120646.0 010 $a3-031-22438-8 024 7 $a10.1007/978-3-031-22438-6 035 $a(MiAaPQ)EBC7203073 035 $a(Au-PeEL)EBL7203073 035 $a(CKB)26154972700041 035 $a(DE-He213)978-3-031-22438-6 035 $a(PPN)268209863 035 $a(EXLCZ)9926154972700041 100 $a20230523d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBayesian tensor decomposition for signal processing and machine learning $emodeling, tuning-free algorithms and applications /$fLei Cheng, Zhongtao Chen, and Yik-Chung Wu 205 $a1st ed. 2023. 210 1$aCham, Switzerland :$cSpringer,$d[2023] 210 4$dİ2023 215 $a1 online resource (189 pages) 311 08$aPrint version: Cheng, Lei Bayesian Tensor Decomposition for Signal Processing and Machine Learning Cham : Springer International Publishing AG,c2023 9783031224379 327 $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. 330 $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. 606 $aMachine learning$xStatistical methods 606 $aSignal processing$xStatistical methods 615 0$aMachine learning$xStatistical methods. 615 0$aSignal processing$xStatistical methods. 676 $a006.31 700 $aCheng$b Lei$01335386 702 $aChen$b Zhongtao 702 $aWu$b Yik-Chung 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910672446803321 996 $aBayesian Tensor Decomposition for Signal Processing and Machine Learning$93049326 997 $aUNINA