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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Visual Interactive Process Monitoringjourney in this chapter. For a pedagogical purpose, the first treatment is given on the most fundamental tensor decomposition format, namely CPD, which has been introduced in Chap. .. As will be demonstrated in the following chapters, the key ideas developed for Bayesian CPD can be applied to other

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Takafumi Yamashita,Ryosuke Sagaame time. Obviously, this is no longer suitable for large datasets. To enable Bayesian tensor CPD in the Big Data era, the idea of stochastic optimization can be incorporated, rendering a scalable algorithm that only processes a mini-batch data at a time. In this chapter, we develop a scalable algor

Hla461

Yuichi Bannai,Takayuki Kosaka,Naomi Aiba usually has additional prior structural information for the factor matrices, e.g., nonnegativeness and orthogonality. Encoding this structural information into the probabilistic tensor modeling while still achieving tractable inference remains a critical challenge. In this chapter, we introduce the

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Lecture Notes in Computer Sciencee tensors can be observed. This gives rise to the tensor completion problem. In this chapter, we use subspace identification for direction-of-arrival (DOA) estimation as a case study to elucidate the key idea of the associated Bayesian modeling and inference in data completion. In particular, we fir

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Lecture Notes in Computer Sciencenformation exists or the data structure is altered. In this chapter, we present tensor rank learning for other tensor decomposition formats. It turns out that what has been presented for CPD is instrumental for other Bayesian tensor modelings, as they share many common characteristics.

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https://doi.org/10.1007/978-3-031-22438-6Structured Tensor Decomposition; Tensor Rank; Automatic Rank Determination; Tensor Signal Processing; Ba

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978-3-031-22440-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl