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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书目名称Bayesian Tensor Decomposition for Signal Processing and Machine Learning网络公开度学科排名




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书目名称Bayesian Tensor Decomposition for Signal Processing and Machine Learning被引频次学科排名




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书目名称Bayesian Tensor Decomposition for Signal Processing and Machine Learning读者反馈学科排名

expunge

Bayesian Learning for Sparsity-Aware Modeling,lection, are highlighted. These merits shed light on the design of sparsity-promoting prior for automating the model pruning in recent machine learning models, including deep neural networks, Gaussian processes, and tensor decompositions. Then, we introduce the variational inference framework for al

箴言

,Bayesian Tensor CPD: Modeling and Inference,journey 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

哑剧

,Bayesian Tensor CPD: Performance and Real-World Applications,the introduced algorithms in the previous chapter. Since the GH prior provides a more flexible sparsity-aware modeling than the Gaussian-gamma prior, it has the potential to act as a better regularizer against noise corruption and to adapt to a wider range of sparsity levels. Numerical studies have

有组织

Insul岛

Bayesian Tensor CPD with Nonnegative Factors, 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

Volatile-Oils

Complex-Valued CPD, Orthogonality Constraint, and Beyond Gaussian Noises,ently occurs in applications including wireless communications and sensor array signal processing. In addition, we have not touched on the design of Bayesian CPD that incorporates the orthogonality structure and/or handles non-Gaussian noises. In this chapter, we present a unified Bayesian modeling

Assault

,Handling Missing Value: A Case Study in Direction-of-Arrival Estimation,e 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

不知疲倦

From CPD to Other Tensor Decompositions,nformation 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.

inspiration

,Bayesian Tensor CPD: Modeling and Inference,rior, and introduce its widely adopted special case, namely Bayesian CPD using Gaussian-Gamma (GG) prior. At the end of this chapter, we introduce a different class of probabilistic modeling, namely non-parametric modeling, and present multiplicative gamma process (MGP) prior as an example.