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Zhongtao Chen

Publications
2
Citations
27
Publications by Year
20222023
Co-Authors:
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Zhongtao Chen

Affiliation

Department of EEE, The University of Hong Kong

Publication Topics

Biography

Zhongtao Chen received the B.Eng. degree from The Chinese University of Hong Kong, Shenzhen, China, in 2021. He is currently working toward the Ph.D. degree with The University of Hong Kong, Hong Kong. His research interests include signal processing and machine learning using Bayesian methods.(Based on document published on 1 April 2022).
Publications
2
Citations
27
Publications by Year
20222023
Co-Authors:
Author's Published Works

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Enhanced Tensor Rank Learning in Bayesian PARAFAC2 for Noisy Irregular Tensor Data

Zhongtao Chen;Lei Cheng;Yik-Chung Wu

2023 31st European Signal Processing Conference (EUSIPCO)
Year: 2023 | Conference Paper |
To analyze irregular multi-dimensional data with unaligned dimensions, which frequently appear in real-world signal processing and machine learning tasks, parallel factor analysis 2 (PARAFAC2) has become the state-of-the-art (SOTA) tensor model that yields interpretable learning results. Like other tensor decomposition models, tensor rank learning in PARAFAC2 is vital to overcome overfitting/under...Show More

Enhanced Tensor Rank Learning in Bayesian PARAFAC2 for Noisy Irregular Tensor Data

Zhongtao Chen;Lei Cheng;Yik-Chung Wu

Year: 2023 | Conference Paper |

Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using the Generalized Hyperbolic Prior

Lei Cheng;Zhongtao Chen;Qingjiang Shi;Yik-Chung Wu;Sergios Theodoridis

IEEE Transactions on Signal Processing
Year: 2022 | Volume: 70 | Journal Article |
Cited by: Papers (27)
Tensor rank learning for canonical polyadic decomposition (CPD) has long been deemed as an essential yet challenging problem. In particular, since thetensor rank controls the complexity of the CPD model, its inaccurate learning would cause overfitting to noise or underfitting to the signal sources, and even destroy the interpretability of model parameters. However, the optimal determination of a t...Show More

Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using the Generalized Hyperbolic Prior

Lei Cheng;Zhongtao Chen;Qingjiang Shi;Yik-Chung Wu;Sergios Theodoridis

Year: 2022 | Volume: 70 | Journal Article |

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