Feb 16, 2023 · Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods. This book presents the foundations of Bayesian inference and introduces recent advances for structured tensor canonical polyadic decompositions.

signal processing and machine learning tasks. Chapter 7 discusses the extension of Bayesian methods to complex-valued data, handling orthogonal constraints and outliers. Chapter 8 uses the direction-of-arrival estimation, which has been one of the focuses of array signal processing for decades, as a case study to introduce the

Jan 1, 2023 · Having introduced the basic philosophies of Bayesian sparsity-aware learning in the last chapter, we formally start our Bayesian tensor decomposition journey in this chapter.

the focuses of array signal processing for decades, as a case study to introduce the Bayesian tensor decomposition under missing data. Finally, Chap. 9 extends the

The material covered includes tensor rank and rank decomposition; basic tensor factorization models and their relationships and properties (including fairly good coverage of identifiability); broad coverage of algorithms ranging from alternating optimization to stochastic gradient; statistical performance analysis; and applications ranging from ...

Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods.

Feb 17, 2023 · In the following subsections, we introduce three widely used tensor decomposition formats with increasing complexity in modeling, namely canonical polyadic decomposition (CPD), Tucker decomposition (TuckerD), and tensor train decomposition (TTD).

Mar 7, 2025 · To address these issues within a unified framework, we propose a fully Bayesian treatment of nonnegative tensor completion with automatic rank determination. Benefitting from the Bayesian framework and the hierarchical sparsity-inducing priors, the model can provide uncertainty estimates of nonnegative latent factors and effectively obtain low ...

Feb 16, 2023 · Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods.