H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 33 Citations 5,478 276 World Ranking 6893 National Ranking 198

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Junbin Gao mainly focuses on Artificial intelligence, Pattern recognition, Cluster analysis, Algorithm and Wavelet packet decomposition. Junbin Gao has researched Artificial intelligence in several fields, including Matching, Tensor and Computer vision. His study in Pattern recognition is interdisciplinary in nature, drawing from both Contextual image classification and Feature.

The Cluster analysis study combines topics in areas such as Graph, Subspace topology, Sparse matrix and Representation. His studies in Subspace topology integrate themes in fields like Data modeling and Euclidean space. His Algorithm study incorporates themes from Kernel and Relevance vector machine.

His most cited work include:

  • Laplacian Regularized Low-Rank Representation and Its Applications (221 citations)
  • Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization (199 citations)
  • Deep adaptive feature embedding with local sample distributions for person re-identification (156 citations)

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

Artificial intelligence, Pattern recognition, Algorithm, Cluster analysis and Computer vision are his primary areas of study. His work is dedicated to discovering how Artificial intelligence, Machine learning are connected with Gaussian process and other disciplines. His studies deal with areas such as Embedding and Kernel as well as Pattern recognition.

His Algorithm research is multidisciplinary, incorporating perspectives in Matrix, Matrix norm and Mathematical optimization. His research in Cluster analysis intersects with topics in Subspace topology, Representation, Rank and Manifold. As a part of the same scientific study, Junbin Gao usually deals with the Manifold, concentrating on Riemannian geometry and frequently concerns with Applied mathematics.

He most often published in these fields:

  • Artificial intelligence (62.20%)
  • Pattern recognition (37.80%)
  • Algorithm (21.71%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (62.20%)
  • Pattern recognition (37.80%)
  • Algorithm (21.71%)

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

Junbin Gao spends much of his time researching Artificial intelligence, Pattern recognition, Algorithm, Cluster analysis and Manifold. His work in Artificial intelligence tackles topics such as Machine learning which are related to areas like Range. He has included themes like Artificial neural network, Feature, Matching and Data set in his Pattern recognition study.

His work deals with themes such as Matrix, Matrix norm, Dimensionality reduction and Rank, which intersect with Algorithm. His Cluster analysis research includes themes of Subspace topology, Graph, Embedding, Representation and Sparse matrix. Junbin Gao interconnects Laplacian matrix, Grassmannian, Applied mathematics, Riemannian geometry and Function in the investigation of issues within Manifold.

Between 2017 and 2021, his most popular works were:

  • Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization (199 citations)
  • Deep adaptive feature embedding with local sample distributions for person re-identification (156 citations)
  • Deep Attention-Based Spatially Recursive Networks for Fine-Grained Visual Recognition (140 citations)

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

  • Artificial intelligence
  • Statistics
  • Machine learning

Junbin Gao focuses on Artificial intelligence, Pattern recognition, Cluster analysis, Convolutional neural network and Feature learning. As part of his studies on Artificial intelligence, Junbin Gao frequently links adjacent subjects like Computer vision. His research integrates issues of Feature, Artificial neural network, Matching, Data set and Laplace operator in his study of Pattern recognition.

His biological study spans a wide range of topics, including Subspace topology, Metric, Representation, Rank and Manifold. His study focuses on the intersection of Representation and fields such as Algorithm with connections in the field of Euclidean space, Linear regression and Image. The various areas that Junbin Gao examines in his Convolutional neural network study include Recurrent neural network, Pooling, Landmark, Jacobian matrix and determinant and Benchmark.

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

A review on applications of wavelet transform techniques in chemical analysis: 1989–1997

Alexander Kai-man Leung;Foo-tim Chau;Jun-bin Gao.
Chemometrics and Intelligent Laboratory Systems (1998)

268 Citations

Laplacian Regularized Low-Rank Representation and Its Applications

Ming Yin;Junbin Gao;Zhouchen Lin.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2016)

268 Citations

Some Remarks on Kalman Filters for the Multisensor Fusion

Junbin Gao;Chris J. Harris.
Information Fusion (2002)

229 Citations

Chemometrics: From Basics to Wavelet Transform

F.T. Chau;Y.Z. Liang;Junbin Gao;X.G. Shao.
(2004)

208 Citations

Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization

Yang Wang;Lin Wu;Xuemin Lin;Junbin Gao.
IEEE Transactions on Neural Networks (2018)

206 Citations

Deep adaptive feature embedding with local sample distributions for person re-identification

Lin Wu;Yang Wang;Junbin Gao;Xue Li.
Pattern Recognition (2018)

156 Citations

A Probabilistic Framework for SVM Regression and Error Bar Estimation

J. B. Gao;S. R. Gunn;C. J. Harris;M. Brown.
Machine Learning (2002)

156 Citations

Deep Attention-Based Spatially Recursive Networks for Fine-Grained Visual Recognition

Lin Wu;Yang Wang;Xue Li;Junbin Gao.
IEEE Transactions on Systems, Man, and Cybernetics (2019)

155 Citations

Where-and-When to Look: Deep Siamese Attention Networks for Video-Based Person Re-Identification

Lin Wu;Yang Wang;Junbin Gao;Xue Li.
IEEE Transactions on Multimedia (2019)

134 Citations

What-and-where to match: Deep spatially multiplicative integration networks for person re-identification

Lin Wu;Yang Wang;Xue Li;Xue Li;Junbin Gao.
Pattern Recognition (2018)

122 Citations

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Best Scientists Citing Junbin Gao

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 21

Zhao Zhang

Zhao Zhang

Hefei University of Technology

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Xuelong Li

Xuelong Li

Northwestern Polytechnical University

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Feiping Nie

Feiping Nie

Northwestern Polytechnical University

Publications: 18

Zenglin Xu

Zenglin Xu

Harbin Institute of Technology

Publications: 16

Sheng Chen

Sheng Chen

University of Southampton

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Yun Fu

Yun Fu

Northeastern University

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Zhouchen Lin

Zhouchen Lin

Peking University

Publications: 14

Meng Wang

Meng Wang

Hefei University of Technology

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Xiaojun Wu

Xiaojun Wu

University of Science and Technology of China

Publications: 14

Yong Xu

Yong Xu

Harbin Institute of Technology

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Yao Zhao

Yao Zhao

Beijing Jiaotong University

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James V. Burke

James V. Burke

University of Washington

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Shengli Xie

Shengli Xie

Guangdong University of Technology

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Enrico Zio

Enrico Zio

Politecnico di Milano

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Zhihua Cai

Zhihua Cai

China University of Geosciences, Wuhan

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