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.
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