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Yipeng Liu
Researcher at University of Electronic Science and Technology of China
Publications - 155
Citations - 2110
Yipeng Liu is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Tensor (intrinsic definition) & Computer science. The author has an hindex of 19, co-authored 125 publications receiving 1189 citations. Previous affiliations of Yipeng Liu include Katholieke Universiteit Leuven.
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Low rank tensor completion for multiway visual data
TL;DR: In this paper, the authors provide an overview of low-rank tensor completion for estimating the missing components of visual data, e.g., color images and videos, and demonstrate the performance comparison when different methods are applied to color image and video processing.
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Improved Robust Tensor Principal Component Analysis via Low-Rank Core Matrix
Yipeng Liu,Longxi Chen,Ce Zhu +2 more
TL;DR: A new TNN is defined that extends TNN with core matrix and a creative algorithm is proposed to deal with RTPCA problems that outperforms state-of-the-art methods in terms of both accuracy and computational complexity.
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Automated Detection of Parkinson’s Disease Based on Multiple Types of Sustained Phonations Using Linear Discriminant Analysis and Genetically Optimized Neural Network
TL;DR: The experimental results suggest that the proposed automated diagnostic system has the potential to classify PD patients from healthy subjects and in future the proposed method can also be exploited for prodromal and differential diagnosis, which are considered challenging tasks.
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Robust Sparse Recovery in Impulsive Noise via $\ell _p$ -$\ell _1$ Optimization
TL;DR: A robust formulation for sparse recovery using the generalized ℓp-norm with 0 ≤ p <; 2 as the metric for the residual error is proposed and compared with some state-of-the-art robust algorithms via numerical simulations to show its improved performance in highly impulsive noise.
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Early diagnosis of Parkinson’s disease from multiple voice recordings by simultaneous sample and feature selection
TL;DR: Experimental results show that the proposed two dimensional data selection method outperforms the state-of-the-art methods in terms of PD detection accuracy on multiple types of voice data.