Bayesian Tensor Decomposition for Signal Processing and Machine Learning: Modeling, Tuning-Free Algorithms, and Applications
Cheng, Lei, Chen, Zhongtao, Wu, Yik-Chung
- 出版商: Springer
- 出版日期: 2024-02-17
- 售價: $5,040
- 貴賓價: 9.5 折 $4,788
- 語言: 英文
- 頁數: 183
- 裝訂: Quality Paper - also called trade paper
- ISBN: 303122440X
- ISBN-13: 9783031224409
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相關分類:
Machine Learning、機率統計學 Probability-and-statistics、Algorithms-data-structures
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相關主題
商品描述
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
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.
- 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.
商品描述(中文翻譯)
本書介紹了結構化張量分解中貝葉斯推斷的最新進展。它解釋了貝葉斯建模和推斷如何導致無需調整的張量分解演算法,這些演算法在許多應用中達到了最先進的性能,包括:
- 盲源分離;
- 社交網路挖掘;
- 圖像和視頻處理;
- 陣列信號處理;以及
- 無線通信。
本書首先介紹了張量和貝葉斯理論的一般主題。接著討論了各種結構化張量分解的概率模型及其推斷演算法,並在相應章節中呈現針對每種張量分解的應用。最後,本書展望未來,探討此研究可以進一步發展的領域。
《貝葉斯張量分解在信號處理和機器學習中的應用》適合對張量數據分析和貝葉斯方法感興趣的研究生和研究人員。