H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 32 Citations 5,398 151 World Ranking 7004 National Ranking 666

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Quantum mechanics
  • Machine learning

Xi Peng mostly deals with Artificial intelligence, Pattern recognition, Subspace topology, Cluster analysis and Embedding. His Artificial intelligence study incorporates themes from Generalization and Computer vision. His studies in Pattern recognition integrate themes in fields like Facial recognition system and Network model.

His Subspace topology research is multidisciplinary, incorporating elements of Sparse approximation, Representation, Mathematical optimization and Data set. The concepts of his Cluster analysis study are interwoven with issues in Graph and Data mining. His work focuses on many connections between Embedding and other disciplines, such as Training set, that overlap with his field of interest in Gaussian noise.

His most cited work include:

  • Accelerating magnetic resonance imaging via deep learning (376 citations)
  • A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts (231 citations)
  • Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering (149 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Image. His Artificial neural network, Embedding, Subspace topology, Deep learning and Training set investigations are all subjects of Artificial intelligence research. His studies deal with areas such as Representation and Cluster analysis as well as Subspace topology.

Xi Peng works mostly in the field of Pattern recognition, limiting it down to concerns involving Graph and, occasionally, Theoretical computer science. He interconnects Facial expression and Compressed sensing in the investigation of issues within Computer vision. His Machine learning study combines topics in areas such as Generalization and Pose.

He most often published in these fields:

  • Artificial intelligence (65.37%)
  • Pattern recognition (26.34%)
  • Computer vision (22.44%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (65.37%)
  • Machine learning (16.10%)
  • Image (13.17%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Artificial intelligence, Machine learning, Image, Artificial neural network and Cluster analysis. His Artificial intelligence research includes themes of Modal, Computer vision and Pattern recognition. His Machine learning research includes elements of Bayesian probability, Generalization, Training set and Joint.

As part of the same scientific family, he usually focuses on Image, concentrating on Representation and intersecting with Range, Trajectory and Noise. As a part of the same scientific family, Xi Peng mostly works in the field of Artificial neural network, focusing on Data set and, on occasion, Iterative reconstruction. His Cluster analysis research is multidisciplinary, incorporating perspectives in Data mining, Graph and Cluster.

Between 2019 and 2021, his most popular works were:

  • Partition level multiview subspace clustering. (45 citations)
  • Deep Clustering With Sample-Assignment Invariance Prior (39 citations)
  • Learning to Learn Single Domain Generalization (29 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Quantum mechanics
  • Machine learning

Xi Peng spends much of his time researching Artificial intelligence, Cluster analysis, Machine learning, Generalization and Deep learning. His work blends Artificial intelligence and Task analysis studies together. His research in Cluster analysis intersects with topics in Data point, Data mining and Graph.

His biological study spans a wide range of topics, including Anomaly detection and Pattern recognition. His Pattern recognition research integrates issues from Recurrent neural network, Anomaly, Fuzzy clustering, Representation and Manifold. His work in Feature extraction addresses issues such as Nonlinear dimensionality reduction, which are connected to fields such as Algorithm.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Accelerating magnetic resonance imaging via deep learning

Shanshan Wang;Zhenghang Su;Leslie Ying;Xi Peng.
international symposium on biomedical imaging (2016)

494 Citations

A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts

Yizhe Zhu;Mohamed Elhoseiny;Bingchen Liu;Xi Peng.
computer vision and pattern recognition (2018)

240 Citations

Structured AutoEncoders for Subspace Clustering.

Xi Peng;Jiashi Feng;Shijie Xiao;Wei-Yun Yau.
IEEE Transactions on Image Processing (2018)

193 Citations

Semantic Graph Convolutional Networks for 3D Human Pose Regression

Long Zhao;Xi Peng;Yu Tian;Mubbasir Kapadia.
computer vision and pattern recognition (2019)

169 Citations

Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering

Xi Peng;Zhiding Yu;Zhang Yi;Huajin Tang.
IEEE Transactions on Systems, Man, and Cybernetics (2017)

167 Citations

Deep subspace clustering with sparsity prior

Xi Peng;Shijie Xiao;Jiashi Feng;Wei-Yun Yau.
international joint conference on artificial intelligence (2016)

160 Citations

Scalable Sparse Subspace Clustering

Xi Peng;Lei Zhang;Zhang Yi.
computer vision and pattern recognition (2013)

140 Citations

Denoising MR Spectroscopic Imaging Data With Low-Rank Approximations

H. M. Nguyen;Xi Peng;M. N. Do;Zhi-Pei Liang.
IEEE Transactions on Biomedical Engineering (2013)

123 Citations

Connections Between Nuclear-Norm and Frobenius-Norm-Based Representations

Xi Peng;Canyi Lu;Zhang Yi;Huajin Tang.
IEEE Transactions on Neural Networks (2018)

122 Citations

A Unified Framework for Representation-Based Subspace Clustering of Out-of-Sample and Large-Scale Data

Xi Peng;Huajin Tang;Lei Zhang;Zhang Yi.
IEEE Transactions on Neural Networks (2016)

115 Citations

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Best Scientists Citing Xi Peng

Jong Chul Ye

Jong Chul Ye

Korea Advanced Institute of Science and Technology

Publications: 30

Xuelong Li

Xuelong Li

Northwestern Polytechnical University

Publications: 26

Zenglin Xu

Zenglin Xu

Harbin Institute of Technology

Publications: 24

Xiaobo Qu

Xiaobo Qu

Chalmers University of Technology

Publications: 23

Zhi-Pei Liang

Zhi-Pei Liang

University of Illinois at Urbana-Champaign

Publications: 20

Huazhu Fu

Huazhu Fu

Agency for Science, Technology and Research

Publications: 20

Feiping Nie

Feiping Nie

Northwestern Polytechnical University

Publications: 19

Yang Yang

Yang Yang

University of Electronic Science and Technology of China

Publications: 18

Zi Huang

Zi Huang

University of Queensland

Publications: 17

Yun Fu

Yun Fu

Northeastern University

Publications: 17

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 17

Ling Shao

Ling Shao

Inception Institute of Artificial Intelligence

Publications: 16

Zhong Chen

Zhong Chen

Nanyang Technological University

Publications: 15

Xin Liu

Xin Liu

Chinese Academy of Sciences

Publications: 15

Vishal M. Patel

Vishal M. Patel

Johns Hopkins University

Publications: 14

Zhang Yi

Zhang Yi

Sichuan University

Publications: 13

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