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Yipeng Liu

Also published under: Yi Peng Liu

Publications
84
Citations
1,621
Publications by Year
20082025
Co-Authors:
Show All Co-Authors (119)
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Yipeng Liu

Also published under: Yi Peng Liu

Affiliation

School of Information and Communication Engineering

Publication Topics

Biography

Yipeng Liu (Senior Member, IEEE) received the BS degree in biomedical engineering and the PhD degree in information and communication engineering from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2006 and 2011, respectively. In 2011, he was a research engineer with Huawei Technologies. From 2011 to 2014, he was a research fellow with the University of Leuven, Leuven, Belgium. In 2014, he joined as an associate professor with the UESTC, where he has been a full professor, since 2023. His research focuses on tensor computation for data processing. He has authored or coauthored more than 100 papers, coauthored two books, “Tensor Computation for Data Analysis” by Springer and “Tensor Regression” in Fo...
Publications
84
Citations
1,621
Publications by Year
20082025
Co-Authors:
Show All Co-Authors (119)
Author's Published Works

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TLRLF4MVC: Tensor Low-Rank and Low-Frequency for Scalable Multi-View Clustering

Zhen Long;Qiyuan Wang;Yazhou Ren;Yipeng Liu;Ce Zhu

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2025 | Early Access Article |
Anchor-based multi-view clustering has garnered much attention for its effectiveness in handling massive datasets. However, current methods either fail to consider intra-view similarity or require (\mathcal {O}(N^{3})) for exploring intra-view similarity, making efficient large-scale multi-view clustering difficult. This paper introduces a novel tensor low-frequency component (TLFC) operator, wh...Show More

TLRLF4MVC: Tensor Low-Rank and Low-Frequency for Scalable Multi-View Clustering

Zhen Long;Qiyuan Wang;Yazhou Ren;Yipeng Liu;Ce Zhu

Year: 2025 | Early Access Article |

Phase-Guided Light Field 3D Imaging with High Pixel Resolution

Geyou Zhang;Kai Liu;Yipeng Liu;Ce Zhu

IEEE Transactions on Instrumentation and Measurement
Year: 2025 | Early Access Article |
On 3D imaging, light field cameras are typically single-shot. However, they often suffer from low spatial resolution and limited depth accuracy. In this paper, by employing an optical projector to project a group of high-frequency phase-shifted sinusoid patterns, we propose a phase-guided light field algorithm to significantly improve both the spatial and depth resolutions for off-the-shelf light ...Show More

Phase-Guided Light Field 3D Imaging with High Pixel Resolution

Geyou Zhang;Kai Liu;Yipeng Liu;Ce Zhu

Year: 2025 | Early Access Article |

Codar: Complex-valued Neural Network for Crossing-Floor Intrusion Detection via WiFi

Weiting Ou;Yipeng Liu;Zhijie Sun;Bing Li;Le Zhang;Ce Zhu

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
WiFi systems offer enormous potential for device-free human intrusion detection. Current methods often require routers to be deployed in multiple adjacent rooms on the same floor, which is redundant and costly. To solve this, we introduce the first work on intrusion detection in the crossing-floor scenario via WiFi. Routers on different floors are utilized without major modifications to the existi...Show More

Codar: Complex-valued Neural Network for Crossing-Floor Intrusion Detection via WiFi

Weiting Ou;Yipeng Liu;Zhijie Sun;Bing Li;Le Zhang;Ce Zhu

Year: 2025 | Conference Paper |

Fully Connected Tensor Network based Brain Structural Feature Extraction for Early Alzheimer’s Disease Detection

Fei He;Xinyue Li;Ce Zhu;Fan Zhang;Yipeng Liu

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Alzheimer’s disease (AD) is an incurable neurodegenerative disease that involves structural changes in the brain. Early diagnosis of AD helps provide timely treatment and delay its progressive process. Many studies have been conducted based on brain images to detect AD. However, these works are mostly designed based on multi-modal brain data. The existing methods on uni-modal data, e.g., diffusion...Show More

Fully Connected Tensor Network based Brain Structural Feature Extraction for Early Alzheimer’s Disease Detection

Fei He;Xinyue Li;Ce Zhu;Fan Zhang;Yipeng Liu

Year: 2025 | Conference Paper |

Finding Channel-wise Tensor Subspaces in GANs

Chao Zhang;Lanlan Feng;Ke Wu;Ce Zhu;Hongwei Guo;Yipeng Liu

2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)
Year: 2024 | Conference Paper |
Generative Adversarial Networks (GANs) can map a latent vector sampled from a random distribution to a photorealistic image. Previous methods attempt to leverage the semantic features embedded in the latent space to enable local editing but encounter challenges in accurately disentangling these features. In this paper, we analyze the redundant characteristics inherent in the deconvolution mechanis...Show More

Finding Channel-wise Tensor Subspaces in GANs

Chao Zhang;Lanlan Feng;Ke Wu;Ce Zhu;Hongwei Guo;Yipeng Liu

Year: 2024 | Conference Paper |

Online Nonconvex Robust Tensor Principal Component Analysis

Lanlan Feng;Yipeng Liu;Ziming Liu;Ce Zhu

IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Early Access Article |
Robust tensor principal component analysis (RTPCA) based on tensor singular value decomposition (t-SVD) separates the low-rank component and the sparse component from the multiway data. For streaming data, online RTPCA (ORTPCA) processes tensor data sequentially, where the low-rank component is updated based on the latest estimation and the newly arrived sample. It enhances both computation and st...Show More

Online Nonconvex Robust Tensor Principal Component Analysis

Lanlan Feng;Yipeng Liu;Ziming Liu;Ce Zhu

Year: 2024 | Early Access Article |

Efficient Black-Box Adversarial Attack on Deep Clustering Models

Nan Yang;Zihan Li;Zhen Long;Xiaolin Huang;Ce Zhu;Yipeng Liu

2024 IEEE International Conference on Image Processing (ICIP)
Year: 2024 | Conference Paper |
Despite the significant progress made by deep clustering models in high-dimensional data processing, they remain vulnerable to adversarial examples. However, research on adversarial attacks against deep clustering algorithms appears to be relatively underexplored. To fill this gap, we propose a query-efficient black-box attack on deep clustering models, which leverages the transferability between ...Show More

Efficient Black-Box Adversarial Attack on Deep Clustering Models

Nan Yang;Zihan Li;Zhen Long;Xiaolin Huang;Ce Zhu;Yipeng Liu

Year: 2024 | Conference Paper |

S2MVTC: A Simple Yet Efficient Scalable Multi-View Tensor Clustering

Zhen Long;Qiyuan Wang;Yazhou Ren;Yipeng Liu;Ce Zhu

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (5)
Anchor-based large-scale multi-view clustering has attracted considerable attention for its effectiveness in handling massive datasets. However, current methods mainly seek the consensus embedding feature for clustering by exploring global correlations between anchor graphs or projection matrices. In this paper, we propose a simple yet efficient scalable multi-view tensor clustering (S2MVTC) appro...Show More

S2MVTC: A Simple Yet Efficient Scalable Multi-View Tensor Clustering

Zhen Long;Qiyuan Wang;Yazhou Ren;Yipeng Liu;Ce Zhu

Year: 2024 | Conference Paper |

A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using DMRI Tractography

Haolin He;Ce Zhu;Le Zhang;Yipeng Liu;Xiao Xu;Yuqian Chen;Leo Zekelman;Jarrett Rushmore;Yogesh Rathi;Nikos Makris;Lauren J. O’Donnell;Fan Zhang

2024 IEEE International Symposium on Biomedical Imaging (ISBI)
Year: 2024 | Conference Paper |
The amygdala plays a vital role in emotional processing and exhibits structural diversity that necessitates fine-scale parcellation for a comprehensive understanding of its anatomico-functional correlations. Diffusion MRI tractography is an advanced imaging technique that can estimate the brain’s white matter structural connectivity to potentially reveal the topography of the amygdala for studying...Show More

A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using DMRI Tractography

Haolin He;Ce Zhu;Le Zhang;Yipeng Liu;Xiao Xu;Yuqian Chen;Leo Zekelman;Jarrett Rushmore;Yogesh Rathi;Nikos Makris;Lauren J. O’Donnell;Fan Zhang

Year: 2024 | Conference Paper |

CTU-Level Lagrange Multiplier and QP Adaption for Random Access Hierarchical Coding in VVC

Hongwei Guo;Ce Zhu;Yipeng Liu;Mao Ye;Lei Luo

2024 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)
Year: 2024 | Conference Paper |
Versatile video coding (VVC) adopts a hierarchical coding structure to exploit the inherent temporal rate-distortion (R-D) dependency resulting from inter-prediction, but the scheme does not support at the coding tree unit (CTU)-level. In this paper, we present a CTU-level Lagrange multiplier and quantization parameter (QP) adaptation method in VVC random access (RA) configuration, aimed at enhanc...Show More

CTU-Level Lagrange Multiplier and QP Adaption for Random Access Hierarchical Coding in VVC

Hongwei Guo;Ce Zhu;Yipeng Liu;Mao Ye;Lei Luo

Year: 2024 | Conference Paper |

Epilepsy Detection with Personal Identification Based on Regularized O-minus Decomposition

Da Shen;Zhongrong Wang;Fei He;Zhijie Sun;Ce Zhu;Yipeng Liu

2024 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Epilepsy is a common neurological disease that seriously affects the patient’s life quality. Electroencephalogram (EEG) is an important modality for epilepsy diagnosis and treatment. Here, we propose a tensor-based method using EEG, which can detect patients with seizures and identify them. In this method, we transform EEG signals into high-order tensors and design a regularized O-minus tensor net...Show More

Epilepsy Detection with Personal Identification Based on Regularized O-minus Decomposition

Da Shen;Zhongrong Wang;Fei He;Zhijie Sun;Ce Zhu;Yipeng Liu

Year: 2024 | Conference Paper |

A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges

Xinyu Lin;Yingjie Zhou;Yipeng Liu;Ce Zhu

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2024 | Volume: 46, Issue: 12 | Journal Article |
Cited by: Papers (7)
An image line segment is a fundamental low-level visual feature that delineates straight, slender, and uninterrupted portions of objects and scenarios within images. Detection and description of line segments lay the basis for numerous vision tasks. Although many studies have aimed to detect and describe line segments, a comprehensive review is lacking, obstructing their progress. This study fills...Show More

A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges

Xinyu Lin;Yingjie Zhou;Yipeng Liu;Ce Zhu

Year: 2024 | Volume: 46, Issue: 12 | Journal Article |

CS2DIPs: Unsupervised HSI Super-Resolution Using Coupled Spatial and Spectral DIPs

Yuan Fang;Yipeng Liu;Chong-Yung Chi;Zhen Long;Ce Zhu

IEEE Transactions on Image Processing
Year: 2024 | Volume: 33 | Journal Article |
Cited by: Papers (4)
In recent years, fusing high spatial resolution multispectral images (HR-MSIs) and low spatial resolution hyperspectral images (LR-HSIs) has become a widely used approach for hyperspectral image super-resolution (HSI-SR). Various unsupervised HSI-SR methods based on deep image prior (DIP) have gained wide popularity thanks to no pre-training requirement. However, DIP-based methods often demonstrat...Show More

CS2DIPs: Unsupervised HSI Super-Resolution Using Coupled Spatial and Spectral DIPs

Yuan Fang;Yipeng Liu;Chong-Yung Chi;Zhen Long;Ce Zhu

Year: 2024 | Volume: 33 | Journal Article |

Adaptively Topological Tensor Network for Multi-View Subspace Clustering

Yipeng Liu;Jie Chen;Yingcong Lu;Weiting Ou;Zhen Long;Ce Zhu

IEEE Transactions on Knowledge and Data Engineering
Year: 2024 | Volume: 36, Issue: 11 | Journal Article |
Cited by: Papers (6)
Multi-view subspace clustering employs learned self-representation from multiple tensor decompositions to exploit the low-rank information. However, the data structures embedded with self-representation tensors may vary in different multi-view datasets. Therefore, a pre-defined decomposition may not fully exploit low-rank information from various data, resulting in sub-optimal multi-view clusterin...Show More

Adaptively Topological Tensor Network for Multi-View Subspace Clustering

Yipeng Liu;Jie Chen;Yingcong Lu;Weiting Ou;Zhen Long;Ce Zhu

Year: 2024 | Volume: 36, Issue: 11 | Journal Article |

Multi-Band Speech Tensor Decomposition for Interactive Feature Extraction in Early Dysphagia Screening

Fei He;Yipeng Liu;Da Shen;Yangyang Jiang;Ying Li;Ce Zhu

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Dysphagia is a prevalent symptom in numerous neurological disorders among older adults. Current dysphagia diagnostic systems either involve invasive procedures or necessitate the ingestion of liquids. Some researchers have devised automatic dysphagia detection methods based on vowels that are easy to collect and sensitive to vocal cord states. These methods extract features from each vowel separat...Show More

Multi-Band Speech Tensor Decomposition for Interactive Feature Extraction in Early Dysphagia Screening

Fei He;Yipeng Liu;Da Shen;Yangyang Jiang;Ying Li;Ce Zhu

Year: 2024 | Conference Paper |

Phase Retrieval by Tensor Total Least Squares

Jiani Liu;Ce Zhu;Yang Chen;Xiaolin Huang;Yipeng Liu

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
Phase retrieval seeks to reconstruct a series of image sequences from measurements that only capture their magnitudes. Current approaches either flatten and stack the image sequences, disregarding their multidimensional structural information, or fail to account for errors within the sensing vectors/tensors. To address these two issues simultaneously, we propose a unified framework for the phase r...Show More

Phase Retrieval by Tensor Total Least Squares

Jiani Liu;Ce Zhu;Yang Chen;Xiaolin Huang;Yipeng Liu

Year: 2024 | Conference Paper |

Tensor Completion for Alzheimer's Disease Prediction From Diffusion Tensor Imaging

Yixin Gou;Yipeng Liu;Fei He;Borbála Hunyadi;Ce Zhu

IEEE Transactions on Biomedical Engineering
Year: 2024 | Volume: 71, Issue: 7 | Journal Article |
Cited by: Papers (1)
Objective: Alzheimer's disease (AD) is a slowly progressive neurodegenerative disorder with insidious onset. Accurate prediction of the disease progression has received increasing attention. Cognitive scores that reflect patients' cognitive status have become important criteria for predicting AD. Most existing methods consider the relationship between neuroimages and cognitive scores to improve pr...Show More

Tensor Completion for Alzheimer's Disease Prediction From Diffusion Tensor Imaging

Yixin Gou;Yipeng Liu;Fei He;Borbála Hunyadi;Ce Zhu

Year: 2024 | Volume: 71, Issue: 7 | Journal Article |

Low Rank Optimization for Efficient Deep Learning: Making a Balance Between Compact Architecture And Fast Training

Xinwei Ou;Zhangxin Chen;Ce Zhu;Yipeng Liu

Journal of Systems Engineering and Electronics
Year: 2024 | Volume: 35, Issue: 3 | Journal Article |
Cited by: Papers (1)
Deep neural networks (DNNs) have achieved great success in many data processing applications. However, high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices, and it is not environmental-friendly with much power cost. In this paper, we focus on low-rank optimization for efficient deep learning techniques. In the space domain, DNNs are...Show More

Low Rank Optimization for Efficient Deep Learning: Making a Balance Between Compact Architecture And Fast Training

Xinwei Ou;Zhangxin Chen;Ce Zhu;Yipeng Liu

Year: 2024 | Volume: 35, Issue: 3 | Journal Article |

Feature Space Recovery for Efficient Incomplete Multi-View Clustering

Zhen Long;Ce Zhu;Pierre Comon;Yazhou Ren;Yipeng Liu

IEEE Transactions on Knowledge and Data Engineering
Year: 2024 | Volume: 36, Issue: 9 | Journal Article |
The tensor singular value decomposition (t-SVD) based incomplete multi-view clustering (IMVC) has received wide attention due to its ability to capture high-order correlations. However, t-SVD suffers from rotation sensitivity, failing to fully explore both inter- and intra-view consistencies. Besides, current methods mainly consider inter- or intra-view correlations, ignoring the low-rank informat...Show More

Feature Space Recovery for Efficient Incomplete Multi-View Clustering

Zhen Long;Ce Zhu;Pierre Comon;Yazhou Ren;Yipeng Liu

Year: 2024 | Volume: 36, Issue: 9 | Journal Article |

TS-RTPM-Net: Data-Driven Tensor Sketching for Efficient CP Decomposition

Xingyu Cao;Xiangtao Zhang;Ce Zhu;Jiani Liu;Yipeng Liu

IEEE Transactions on Big Data
Year: 2024 | Volume: 10, Issue: 1 | Journal Article |
Tensor decomposition is widely used in feature extraction, data analysis, and other fields. As a means of tensor decomposition, the robust tensor power method based on tensor sketch (TS-RTPM) can quickly mine the potential features of tensor, but in some cases, its approximation performance is limited. In this paper, we propose a data-driven framework called TS-RTPM-Net, which improves the estimat...Show More

TS-RTPM-Net: Data-Driven Tensor Sketching for Efficient CP Decomposition

Xingyu Cao;Xiangtao Zhang;Ce Zhu;Jiani Liu;Yipeng Liu

Year: 2024 | Volume: 10, Issue: 1 | Journal Article |

Multi-View MERA Subspace Clustering

Zhen Long;Ce Zhu;Jie Chen;Zihan Li;Yazhou Ren;Yipeng Liu

IEEE Transactions on Multimedia
Year: 2024 | Volume: 26 | Journal Article |
Cited by: Papers (9)
Tensor-based multi-view subspace clustering (MSC) can capture high-order correlation in the self-representation tensor. Current tensor decompositions for MSC suffer from highly unbalanced unfolding matrices or rotation sensitivity, failing to fully explore inter/intra-view information. Using the advanced tensor network, namely, multi-scale entanglement renormalization ansatz (MERA), we propose a l...Show More

Multi-View MERA Subspace Clustering

Zhen Long;Ce Zhu;Jie Chen;Zihan Li;Yazhou Ren;Yipeng Liu

Year: 2024 | Volume: 26 | Journal Article |

O-Minus Decomposition for Multiview Tensor Subspace Clustering

Yingcong Lu;Yipeng Liu;Zhen Long;Zhangxin Chen;Ce Zhu

IEEE Transactions on Artificial Intelligence
Year: 2024 | Volume: 5, Issue: 3 | Journal Article |
Cited by: Papers (7)
With the powerful ability to exploit the latent structure of self-representation information, multiple off-the-shelf low rank tensor constraints have been employed in multiview tensor subspace clustering (MTSC) for achieving significant performance. However, current approaches mainly suffer from a series of problems, such as the deficient exploration of self-representation due to the unbalanced un...Show More

O-Minus Decomposition for Multiview Tensor Subspace Clustering

Yingcong Lu;Yipeng Liu;Zhen Long;Zhangxin Chen;Ce Zhu

Year: 2024 | Volume: 5, Issue: 3 | Journal Article |

A novel signal presence detection strategy in beamforming spectra for weak signal detection

Kehao Zhang;Silong Tang;Ning Liu;Kai Chang;Yipeng Liu;Qun Wan

IET International Radar Conference (IRC 2023)
Year: 2023 | Conference Paper |
In complex electromagnetic environments, passive radio direction determination often encounters a challenge where distant weak signals and nearby strong signals are difficult to separate in both the time and frequency domains. Weak signals are often masked by the presence of strong signals, making detection and localization problematic. To address this issue, this paper presents an approach that l...Show More

A novel signal presence detection strategy in beamforming spectra for weak signal detection

Kehao Zhang;Silong Tang;Ning Liu;Kai Chang;Yipeng Liu;Qun Wan

Year: 2023 | Conference Paper |

Optimal Low-Rank Tensor Tree Completion

Zihan Li;Ce Zhu;Zhen Long;Yipeng Liu

2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP)
Year: 2023 | Conference Paper |
Tensor completion is a powerful technique for recovering missing entries from partial observations. Tensor tree network, with a hierarchical structure, has gained widespread attention for its ability to balance and effectively explore the correlations in high-order data. However, the performance of tensor trees is influenced by the order of modes, leading to variations in their effectiveness. To a...Show More

Optimal Low-Rank Tensor Tree Completion

Zihan Li;Ce Zhu;Zhen Long;Yipeng Liu

Year: 2023 | Conference Paper |

Effective and Efficient Line Segment Detection for Visual Measurement Guided by Level Lines

Xinyu Lin;Yingjie Zhou;Yipeng Liu;Ce Zhu

IEEE Transactions on Instrumentation and Measurement
Year: 2023 | Volume: 72 | Journal Article |
Cited by: Papers (24)
Line segment detection is the basis for various visual measurement tasks. Numerous methods have been proposed to detect line segments from images, and edge-fitting-based ones have gained significant attention because of their remarkable detection efficiency. However, most edge-fitting-based methods primarily rely on gradient magnitude for edge detection and edge coordinates for line segment fittin...Show More

Effective and Efficient Line Segment Detection for Visual Measurement Guided by Level Lines

Xinyu Lin;Yingjie Zhou;Yipeng Liu;Ce Zhu

Year: 2023 | Volume: 72 | Journal Article |

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