What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Xuelong Li spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Machine learning.
His work in Image processing, Cluster analysis, Pixel, Linear discriminant analysis and Discriminative model are all subfields of Artificial intelligence research.
The concepts of his Pattern recognition study are interwoven with issues in Image, Iterative reconstruction and Feature.
As a part of the same scientific family, Xuelong Li mostly works in the field of Computer vision, focusing on Pattern recognition and, on occasion, Face.
His Feature extraction research is multidisciplinary, relying on both Subspace topology, Histogram, Sparse approximation, Feature learning and Feature selection.
His work carried out in the field of Machine learning brings together such families of science as Contextual image classification, Visualization and Robustness.
His most cited work include:
- General Tensor Discriminant Analysis and Gabor Features for Gait Recognition (1012 citations)
- Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval (783 citations)
- Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (712 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Cluster analysis.
His research links Machine learning with Artificial intelligence.
His study looks at the intersection of Pattern recognition and topics like Subspace topology with Linear discriminant analysis.
Xuelong Li interconnects Visualization and Feature selection in the investigation of issues within Feature extraction.
His Cluster analysis research incorporates themes from Algorithm, Graph, Data mining and Data set.
His Graph research is multidisciplinary, incorporating elements of Theoretical computer science and Graph.
He most often published in these fields:
- Artificial intelligence (72.73%)
- Pattern recognition (44.56%)
- Computer vision (27.17%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (72.73%)
- Pattern recognition (44.56%)
- Cluster analysis (13.69%)
In recent papers he was focusing on the following fields of study:
Xuelong Li mostly deals with Artificial intelligence, Pattern recognition, Cluster analysis, Algorithm and Graph.
His work deals with themes such as Machine learning and Computer vision, which intersect with Artificial intelligence.
He works mostly in the field of Pattern recognition, limiting it down to topics relating to Subspace topology and, in certain cases, Linear discriminant analysis and Constraint, as a part of the same area of interest.
His research in Cluster analysis intersects with topics in Embedding, Outlier and Data set.
His biological study spans a wide range of topics, including Matrix decomposition, Manifold, Feature selection and Robustness.
His Graph study integrates concerns from other disciplines, such as Data modeling, Autoencoder, Theoretical computer science and Graph.
Between 2019 and 2021, his most popular works were:
- Detecting Coherent Groups in Crowd Scenes by Multiview Clustering (143 citations)
- Ternary Adversarial Networks With Self-Supervision for Zero-Shot Cross-Modal Retrieval (83 citations)
- Video Summarization With Attention-Based Encoder–Decoder Networks (76 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Xuelong Li mainly focuses on Artificial intelligence, Pattern recognition, Feature extraction, Cluster analysis and Algorithm.
His Artificial intelligence study frequently links to other fields, such as Machine learning.
His Pattern recognition research integrates issues from Entropy and Feature.
His work in Feature extraction tackles topics such as Artificial neural network which are related to areas like Fuzzy clustering.
His Cluster analysis study combines topics in areas such as Matrix decomposition, Manifold, Graph and Graph.
The Algorithm study combines topics in areas such as Embedding, Field, Outlier, Kernel and Robustness.
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