Bayesian Tensor Decomposition for Signal Processing and Machine Learning Modeling, Tuning-Free Algorithms, and Applications /
This 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; soci...
Gardado en:
| Main Authors: | , , |
|---|---|
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
|
| Edición: | 1st ed. 2023. |
| Subjects: | |
| Acceso en liña: | https://doi.org/10.1007/978-3-031-22438-6 |
| Tags: |
Engadir etiqueta
Sen Etiquetas, Sexa o primeiro en etiquetar este rexistro!
|
Table of Contents:
- Tensor 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.