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.
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