2023 - Research.com Computer Science in China Leader Award
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.
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.
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.
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.
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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)
Minimum redundancy feature selection from microarray gene expression data.
Chris H. Q. Ding;Hanchuan Peng.
Journal of Bioinformatics and Computational Biology (2005)
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)
K-means clustering via principal component analysis
Chris Ding;Xiaofeng He.
international conference on machine learning (2004)
Orthogonal nonnegative matrix t-factorizations for clustering
Chris Ding;Tao Li;Wei Peng;Haesun Park.
knowledge discovery and data mining (2006)
Convex and Semi-Nonnegative Matrix Factorizations
C.H.Q. Ding;Tao Li;M.I. Jordan.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)
On the Equivalence of Nonnegative Matrix Factorization and Spectral Clustering.
Chris H. Q. Ding;Xiaofeng He.
siam international conference on data mining (2005)
Multi-class protein fold recognition using support vector machines and neural networks.
Chris H.Q. Ding;Inna Dubchak.
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)
Spectral Relaxation for K-means Clustering
Hongyuan Zha;Xiaofeng He;Chris Ding;Ming Gu.
neural information processing systems (2001)
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