What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
His primary scientific interests are in Artificial intelligence, Machine learning, Feature selection, Algorithm design and Feature vector.
His research investigates the link between Artificial intelligence and topics such as Eigendecomposition of a matrix that cross with problems in Discriminative model.
His work on Recommender system as part of general Machine learning research is frequently linked to Event, TRECVID and Baseline, thereby connecting diverse disciplines of science.
His Feature selection research incorporates themes from Data mining and Task.
Xiaojun Chang focuses mostly in the field of Algorithm design, narrowing it down to matters related to Dimensionality reduction and, in some cases, Orthographic projection, Computation and Cluster analysis.
He has researched Pattern recognition in several fields, including Computational complexity theory, Graph and Laplacian matrix.
His most cited work include:
- Semantic Pooling for Complex Event Analysis in Untrimmed Videos (263 citations)
- Multi-Class Active Learning by Uncertainty Sampling with Diversity Maximization (222 citations)
- Bi-Level Semantic Representation Analysis for Multimedia Event Detection (196 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, Pattern recognition, Feature selection and TRECVID.
In Artificial intelligence, Xiaojun Chang works on issues like Data mining, which are connected to Training set.
His studies in Machine learning integrate themes in fields like Graph and Robustness.
His biological study spans a wide range of topics, including Algorithm, Theoretical computer science and Graph.
His research in Pattern recognition focuses on subjects like Computational complexity theory, which are connected to Robust principal component analysis and Maximization.
His Feature selection study combines topics from a wide range of disciplines, such as Feature extraction, Principal component analysis, Feature and Feature vector.
He most often published in these fields:
- Artificial intelligence (80.52%)
- Machine learning (50.65%)
- Pattern recognition (31.17%)
What were the highlights of his more recent work (between 2017-2020)?
- Artificial intelligence (80.52%)
- Machine learning (50.65%)
- Graph (12.99%)
In recent papers he was focusing on the following fields of study:
Xiaojun Chang mostly deals with Artificial intelligence, Machine learning, Graph, Cluster analysis and Feature vector.
His Artificial intelligence research is multidisciplinary, relying on both Natural language processing and Pattern recognition.
Machine learning is frequently linked to Topic model in his study.
His work carried out in the field of Graph brings together such families of science as Algorithm and Algorithm design.
His research integrates issues of Lasso, Graph and Dimensionality reduction in his study of Cluster analysis.
His Feature vector study incorporates themes from Computational complexity theory, Feature selection and Projection.
Between 2017 and 2020, his most popular works were:
- MMALFM: Explainable Recommendation by Leveraging Reviews and Images (105 citations)
- Adaptive Unsupervised Feature Selection With Structure Regularization (95 citations)
- An Adaptive Semisupervised Feature Analysis for Video Semantic Recognition (77 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Pattern recognition
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Pattern recognition, Graph and Laplacian matrix.
His work on Segmentation, Inference and Object as part of his general Artificial intelligence study is frequently connected to Process and Function, thereby bridging the divide between different branches of science.
Machine learning is often connected to Benchmark in his work.
The Pattern recognition study combines topics in areas such as Computational complexity theory and Deep learning.
His Graph research includes elements of Cluster analysis and Dimensionality reduction.
His Laplacian matrix research is multidisciplinary, incorporating elements of Embedding and Discriminative model.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
