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Bayesian Tensor Decomposition for Signal Processing and Machine Learning

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Cover of 'Bayesian Tensor Decomposition for Signal Processing and Machine Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Tensor Decomposition: Basics, Algorithms, and Recent Advances
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    Chapter 2 Bayesian Learning for Sparsity-Aware Modeling
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    Chapter 3 Bayesian Tensor CPD: Modeling and Inference
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    Chapter 5 When Stochastic Optimization Meets VI: Scaling Bayesian CPD to Massive Data
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    Chapter 6 Bayesian Tensor CPD with Nonnegative Factors
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    Chapter 7 Complex-Valued CPD, Orthogonality Constraint, and Beyond Gaussian Noises
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    Chapter 8 Handling Missing Value: A Case Study in Direction-of-Arrival Estimation
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    Chapter 9 From CPD to Other Tensor Decompositions
Attention for Chapter 1: Tensor Decomposition: Basics, Algorithms, and Recent Advances
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Chapter title
Tensor Decomposition: Basics, Algorithms, and Recent Advances
Chapter number 1
Book title
Bayesian Tensor Decomposition for Signal Processing and Machine Learning
Published by
Springer, Cham, January 2023
DOI 10.1007/978-3-031-22438-6_1
Book ISBNs
978-3-03-122437-9, 978-3-03-122438-6
Authors

Cheng, Lei, Chen, Zhongtao, Wu, Yik-Chung

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