H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 73 Citations 35,948 239 World Ranking 670 National Ranking 55

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary areas of investigation include Artificial intelligence, Cluster analysis, Pattern recognition, Non-negative matrix factorization and Data mining. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Machine learning. His research in Cluster analysis intersects with topics in Adjacency matrix, Algorithm, Subspace topology and Graph partition.

The study incorporates disciplines such as Norm and Outlier in addition to Pattern recognition. His studies in Non-negative matrix factorization integrate themes in fields like Discrete mathematics, Spectral clustering, Nonnegative matrix and Combinatorics. His biological study deals with issues like Redundancy, which deal with fields such as Mutual information.

His most cited work include:

  • Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy (6404 citations)
  • Minimum redundancy feature selection from microarray gene expression data. (1466 citations)
  • Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization (1178 citations)

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

Chris Ding spends much of his time researching Artificial intelligence, Pattern recognition, Cluster analysis, Data mining and Algorithm. In his study, Protein function prediction is strongly linked to Machine learning, which falls under the umbrella field of Artificial intelligence. His Pattern recognition research integrates issues from Subspace topology, Norm and Feature.

His study looks at the relationship between Cluster analysis and topics such as Non-negative matrix factorization, which overlap with Nonnegative matrix and Discrete mathematics. His Data mining study combines topics from a wide range of disciplines, such as Singular value decomposition, Information retrieval and Clustering high-dimensional data. His studies deal with areas such as Graph and Mathematical optimization as well as Algorithm.

He most often published in these fields:

  • Artificial intelligence (49.04%)
  • Pattern recognition (32.69%)
  • Cluster analysis (29.81%)

What were the highlights of his more recent work (between 2016-2021)?

  • Artificial intelligence (49.04%)
  • Pattern recognition (32.69%)
  • Regularization (5.13%)

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

Artificial intelligence, Pattern recognition, Regularization, Robustness and Algorithm are his primary areas of study. His Artificial intelligence research incorporates elements of Machine learning and Computer vision. His Pattern recognition study integrates concerns from other disciplines, such as Norm and Lasso.

The Regularization study combines topics in areas such as Segmentation, Recommender system, Singular value decomposition and Mutual information. His study in Outlier is interdisciplinary in nature, drawing from both Data mining and Error function. The various areas that Chris Ding examines in his Dimensionality reduction study include Feature extraction and Linear discriminant analysis.

Between 2016 and 2021, his most popular works were:

  • Exercise-Enhanced Sequential Modeling for Student Performance Prediction. (45 citations)
  • Transductive Semi-Supervised Deep Learning using Min-Max Features (42 citations)
  • ${R}_1$ -2-DPCA and Face Recognition (28 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Robustness, Dimensionality reduction and Principal component analysis. His Artificial intelligence research includes elements of Optimization problem and Machine learning. His research in Pattern recognition is mostly focused on Class.

His Robustness research is multidisciplinary, incorporating perspectives in Iterative method, Iterative reconstruction, Supervised learning and Cluster analysis. Chris Ding interconnects Tensor, Tensor, Representation, Laplacian matrix and Manifold in the investigation of issues within Cluster analysis. His work carried out in the field of Dimensionality reduction brings together such families of science as Feature extraction and Linear discriminant analysis.

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.

Top Publications

Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy

Hanchuan Peng;Fuhui Long;C. Ding.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)

8782 Citations

Minimum redundancy feature selection from microarray gene expression data.

Chris H. Q. Ding;Hanchuan Peng.
Journal of Bioinformatics and Computational Biology (2005)

2425 Citations

K-means clustering via principal component analysis

Chris Ding;Xiaofeng He.
international conference on machine learning (2004)

1657 Citations

Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization

Feiping Nie;Heng Huang;Xiao Cai;Chris H. Ding.
neural information processing systems (2010)

1494 Citations

Orthogonal nonnegative matrix t-factorizations for clustering

Chris Ding;Tao Li;Wei Peng;Haesun Park.
knowledge discovery and data mining (2006)

1120 Citations

Multi-class protein fold recognition using support vector machines and neural networks.

Chris H.Q. Ding;Inna Dubchak.
Bioinformatics (2001)

1103 Citations

Convex and Semi-Nonnegative Matrix Factorizations

C.H.Q. Ding;Tao Li;M.I. Jordan.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)

1087 Citations

A min-max cut algorithm for graph partitioning and data clustering

C.H.Q. Ding;Xiaofeng He;Xiaofeng He;Hongyuan Zha;Ming Gu.
international conference on data mining (2001)

1085 Citations

On the Equivalence of Nonnegative Matrix Factorization and Spectral Clustering.

Chris H. Q. Ding;Xiaofeng He.
siam international conference on data mining (2005)

1069 Citations

Spectral Relaxation for K-means Clustering

Hongyuan Zha;Xiaofeng He;Chris Ding;Ming Gu.
neural information processing systems (2001)

793 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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