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, Data mining, Machine learning, Data stream mining and Classifier.
The Artificial intelligence study combines topics in areas such as Algorithm design, Graph and Pattern recognition.
His studies deal with areas such as Embedding, Data quality, Information retrieval and Leverage as well as Data mining.
His work in the fields of Machine learning, such as Ensemble learning and Semi-supervised learning, intersects with other areas such as Proactive learning and Active learning.
His studies in Data stream mining integrate themes in fields like Data modeling, Data stream, Data collection and Data pre-processing.
He has included themes like Disjoint sets and Group method of data handling in his Classifier study.
His most cited work include:
- Data mining with big data (1756 citations)
- Class noise vs. attribute noise: a quantitative study of their impacts (540 citations)
- Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR) (241 citations)
What are the main themes of his work throughout his whole career to date?
Xingquan Zhu mainly focuses on Artificial intelligence, Machine learning, Data mining, Theoretical computer science and Pattern recognition.
His Artificial intelligence research is multidisciplinary, relying on both Computer vision and Graph.
The Machine learning study combines topics in areas such as Data modeling and Training set.
Xingquan Zhu specializes in Data mining, namely Data stream mining.
His studies deal with areas such as Artificial neural network, Stochastic gradient descent, Graph, Embedding and Feature learning as well as Theoretical computer science.
His biological study spans a wide range of topics, including Video tracking, Video processing and Image retrieval.
He most often published in these fields:
- Artificial intelligence (53.07%)
- Machine learning (35.60%)
- Data mining (35.28%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (53.07%)
- Theoretical computer science (12.94%)
- Feature learning (6.47%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Theoretical computer science, Feature learning, Graph and Graph.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Natural language processing, Machine learning and Pattern recognition.
His research in the fields of Support vector machine overlaps with other disciplines such as Information technology.
His Theoretical computer science study incorporates themes from Artificial neural network, Stochastic gradient descent, Node, Embedding and Nearest neighbor search.
He interconnects Probabilistic logic, Task analysis and Feature in the investigation of issues within Feature learning.
He usually deals with Graph and limits it to topics linked to Feature extraction and Classifier.
Between 2016 and 2021, his most popular works were:
- Network Representation Learning: A Survey (229 citations)
- MGAE: Marginalized Graph Autoencoder for Graph Clustering (82 citations)
- User profile preserving social network embedding (52 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Artificial intelligence, Graph, Theoretical computer science, Machine learning and Pattern recognition.
His Artificial intelligence study typically links adjacent topics like Algorithm design.
His work on Feature vector as part of general Machine learning research is frequently linked to Information technology, bridging the gap between disciplines.
His Pattern recognition research includes elements of Graph classification and Time series.
The study incorporates disciplines such as Data mining and Benchmark in addition to Flexibility.
His Data mining research is multidisciplinary, incorporating perspectives in Watermark and Data security.
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