What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Pattern recognition
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Pattern recognition, Transfer of learning and Feature learning.
All of his Artificial intelligence and Embedding and Feature vector investigations are sub-components of the entire Artificial intelligence study.
His work in the fields of Semi-supervised learning overlaps with other areas such as Knowledge transfer, Semantic gap and Rank.
The Discriminative model research Zhengming Ding does as part of his general Pattern recognition study is frequently linked to other disciplines of science, such as Traffic sign recognition, therefore creating a link between diverse domains of science.
His Transfer of learning study combines topics in areas such as Subspace topology, Data mining and Robustness.
His biological study spans a wide range of topics, including Classifier and Feature extraction.
His most cited work include:
- Multi-View Clustering via Deep Matrix Factorization. (140 citations)
- Low-Rank Common Subspace for Multi-view Learning (93 citations)
- Robust Transfer Metric Learning for Image Classification (90 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Machine learning, Pattern recognition, Discriminative model and Cluster analysis.
Artificial intelligence and Domain adaptation are two areas of study in which Zhengming Ding engages in interdisciplinary research.
His research in Machine learning tackles topics such as Robustness which are related to areas like Autoencoder.
In the field of Pattern recognition, his study on Segmentation overlaps with subjects such as Data structure.
In his research, Text mining is intimately related to Feature extraction, which falls under the overarching field of Discriminative model.
Representation and Embedding is closely connected to Theoretical computer science in his research, which is encompassed under the umbrella topic of Cluster analysis.
He most often published in these fields:
- Artificial intelligence (87.14%)
- Machine learning (45.71%)
- Pattern recognition (39.29%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (87.14%)
- Pattern recognition (39.29%)
- Machine learning (45.71%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Machine learning, Domain adaptation and Feature.
His Discriminative model, Feature learning, Classifier and Cluster analysis study in the realm of Artificial intelligence interacts with subjects such as Adaptation.
His Cluster analysis study combines topics from a wide range of disciplines, such as Data mining, Missing data, Object, Modality and Data set.
His research in Pattern recognition intersects with topics in Subspace topology and Feature.
His work on Transfer of learning as part of general Machine learning study is frequently linked to Task analysis and Rank, bridging the gap between disciplines.
The study incorporates disciplines such as Artificial neural network, Representation, DUAL, Matching and Hierarchical clustering in addition to Feature.
Between 2019 and 2021, his most popular works were:
- Maximum Density Divergence for Domain Adaptation. (24 citations)
- Deep Residual Correction Network for Partial Domain Adaptation (21 citations)
- Marginalized Multiview Ensemble Clustering (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Pattern recognition
Artificial intelligence, Machine learning, Domain adaptation, Pattern recognition and Transfer of learning are his primary areas of study.
Particularly relevant to Modality is his body of work in Artificial intelligence.
His work on Feature learning is typically connected to Task analysis and Mirna expression as part of general Machine learning study, connecting several disciplines of science.
His Feature learning study incorporates themes from Classifier, Classifier, Artificial neural network, Labeled data and Feature extraction.
His Pattern recognition research includes themes of Feature and Residual.
The Transfer of learning study combines topics in areas such as RGB color model, Training set, Overfitting and Decision tree.
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