Titlebook: Bayesian Tensor Decomposition for Signal Processing and Machine Learning; Modeling, Tuning-Fre Lei Cheng,Zhongtao Chen,Yik-Chung Wu Book 20

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Lecture Notes in Computer Scienceayesian CPD that incorporates the orthogonality structure and/or handles non-Gaussian noises. In this chapter, we present a unified Bayesian modeling and inference for complex-valued tensor CPD with/without orthogonal factors, under/not under Gaussian noises. Applications on blind receiver design and linear image coding are presented.

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Bayesian Learning for Sparsity-Aware Modeling,g models, including deep neural networks, Gaussian processes, and tensor decompositions. Then, we introduce the variational inference framework for algorithm development and discuss its tractability in different Bayesian models.