H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 3,519 155 World Ranking 8354 National Ranking 790

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

Artificial intelligence, Cluster analysis, Multiple kernel learning, Mathematical optimization and Pattern recognition are his primary areas of study. His Artificial intelligence study integrates concerns from other disciplines, such as Matrix decomposition and Machine learning. His study in Cluster analysis is interdisciplinary in nature, drawing from both Data mining, Partition, Outlier, Residual and Kernel.

His work focuses on many connections between Multiple kernel learning and other disciplines, such as Optimization problem, that overlap with his field of interest in Ranking. His Mathematical optimization study combines topics from a wide range of disciplines, such as Radial basis function kernel, Kernel embedding of distributions, Tree kernel, Kernel and Support vector machine. Zenglin Xu studies Feature selection which is a part of Pattern recognition.

His most cited work include:

  • Discriminative Semi-Supervised Feature Selection Via Manifold Regularization (238 citations)
  • Simple and Efficient Multiple Kernel Learning by Group Lasso (194 citations)
  • An Extended Level Method for Efficient Multiple Kernel Learning (137 citations)

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

His primary areas of study are Artificial intelligence, Machine learning, Cluster analysis, Pattern recognition and Theoretical computer science. His work in Representation, Deep learning, Discriminative model, Artificial neural network and Image is related to Artificial intelligence. Zenglin Xu has researched Machine learning in several fields, including Variety and Inference.

The Cluster analysis study combines topics in areas such as Data mining, Outlier, Multiple kernel learning, Kernel and Data set. Zenglin Xu has included themes like Mathematical optimization and Feature selection in his Multiple kernel learning study. Zenglin Xu focuses mostly in the field of Pattern recognition, narrowing it down to matters related to Contextual image classification and, in some cases, Convolutional neural network.

He most often published in these fields:

  • Artificial intelligence (66.50%)
  • Machine learning (32.04%)
  • Cluster analysis (28.16%)

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

  • Artificial intelligence (66.50%)
  • Machine learning (32.04%)
  • Cluster analysis (28.16%)

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

His primary areas of investigation include Artificial intelligence, Machine learning, Cluster analysis, Representation and Theoretical computer science. In his study, which falls under the umbrella issue of Artificial intelligence, Range is strongly linked to Pattern recognition. His Machine learning research includes elements of Variety and Generative grammar.

His Cluster analysis research includes themes of Data point, Optimization problem and Relation, Data mining. His Representation research is multidisciplinary, incorporating elements of Artificial neural network, Similarity, Feature and Feature learning. His biological study deals with issues like Connected component, which deal with fields such as Kernel and Inference.

Between 2019 and 2021, his most popular works were:

  • Robust Graph Learning From Noisy Data (102 citations)
  • Partition level multiview subspace clustering. (45 citations)
  • Multi-graph fusion for multi-view spectral clustering (35 citations)

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

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

His main research concerns Artificial intelligence, Cluster analysis, Theoretical computer science, Pattern recognition and Machine learning. In his study, Boosting is strongly linked to Natural language processing, which falls under the umbrella field of Artificial intelligence. His research in Cluster analysis intersects with topics in Data point, Similarity measure, Data mining and Kernel.

His work in Theoretical computer science covers topics such as Spectral clustering which are related to areas like Data set. His Pattern recognition research includes themes of Contextual image classification and Deep learning. Zenglin Xu has included themes like Generative grammar and Learning methods in his Machine learning study.

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

Discriminative Semi-Supervised Feature Selection Via Manifold Regularization

Zenglin Xu;Irwin King;Michael Rung-Tsong Lyu;Rong Jin.
IEEE Transactions on Neural Networks (2010)

309 Citations

Simple and Efficient Multiple Kernel Learning by Group Lasso

Zenglin Xu;Rong Jin;Haiqin Yang;Irwin King.
international conference on machine learning (2010)

292 Citations

An Extended Level Method for Efficient Multiple Kernel Learning

Zenglin Xu;Rong Jin;Irwin King;Michael Lyu.
neural information processing systems (2008)

194 Citations

Robust Graph Learning From Noisy Data

Zhao Kang;Haiqi Pan;Steven C. H. Hoi;Zenglin Xu.
IEEE Transactions on Systems, Man, and Cybernetics (2020)

152 Citations

Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis

Zenglin Xu;Feng Yan;Alan Qi.
international conference on machine learning (2012)

130 Citations

Online Learning for Group Lasso

Haiqin Yang;Zenglin Xu;Irwin King;Michael R. Lyu.
international conference on machine learning (2010)

116 Citations

Low-rank Kernel Learning for Graph-based Clustering

Zhao Kang;Liangjian Wen;Wenyu Chen;Zenglin Xu.
Knowledge Based Systems (2019)

101 Citations

Efficient Sparse Generalized Multiple Kernel Learning

Haiqin Yang;Zenglin Xu;Jieping Ye;I King.
IEEE Transactions on Neural Networks (2011)

96 Citations

Introduction to Semi-Supervised Learning

Zenglin Xu;Irwin King.
(2015)

94 Citations

Superneurons: dynamic GPU memory management for training deep neural networks

Linnan Wang;Jinmian Ye;Yiyang Zhao;Wei Wu.
acm sigplan symposium on principles and practice of parallel programming (2018)

91 Citations

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Best Scientists Citing Zenglin Xu

Irwin King

Irwin King

Chinese University of Hong Kong

Publications: 25

Steven C. H. Hoi

Steven C. H. Hoi

Singapore Management University

Publications: 24

Michael R. Lyu

Michael R. Lyu

Chinese University of Hong Kong

Publications: 24

Feiping Nie

Feiping Nie

Northwestern Polytechnical University

Publications: 21

Qibin Zhao

Qibin Zhao

RIKEN

Publications: 19

Ivor W. Tsang

Ivor W. Tsang

University of Technology Sydney

Publications: 19

Licheng Jiao

Licheng Jiao

Xidian University

Publications: 17

Rong Jin

Rong Jin

Alibaba Group (China)

Publications: 17

Xuelong Li

Xuelong Li

Northwestern Polytechnical University

Publications: 17

Tianrui Li

Tianrui Li

Southwest Jiaotong University

Publications: 16

Zhao Zhang

Zhao Zhang

Hefei University of Technology

Publications: 15

Andrzej Cichocki

Andrzej Cichocki

Skolkovo Institute of Science and Technology

Publications: 15

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 15

Guoxu Zhou

Guoxu Zhou

Guangdong University of Technology

Publications: 10

Xiao-Yuan Jing

Xiao-Yuan Jing

Wuhan University

Publications: 10

Junbin Gao

Junbin Gao

University of Sydney

Publications: 10

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