使成整体 发表于 2025-3-25 03:26:06

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

Prognosis 发表于 2025-3-25 08:16:11

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magnate 发表于 2025-3-25 15:16:30

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 发表于 2025-3-25 18:31:55

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

intimate 发表于 2025-3-25 21:34:52

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regale 发表于 2025-3-26 04:02:22

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

发起 发表于 2025-3-26 08:10:21

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.

平常 发表于 2025-3-26 09:17:24

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翻布寻找 发表于 2025-3-26 16:25:07

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

拥挤前 发表于 2025-3-26 18:45:17

978-3-031-22440-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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查看完整版本: Titlebook: Bayesian Tensor Decomposition for Signal Processing and Machine Learning; Modeling, Tuning-Fre Lei Cheng,Zhongtao Chen,Yik-Chung Wu Book 20