What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Algorithm, Artificial intelligence, Adaptive filter, Robustness and Entropy.
His Algorithm research is multidisciplinary, relying on both Power, Least squares support vector machine and Kernel, Kernel method.
Badong Chen has included themes like Machine learning, Kernel adaptive filter and Pattern recognition in his Artificial intelligence study.
His Adaptive filter study combines topics from a wide range of disciplines, such as Tracking, Excess mean square error and Mathematical optimization.
His studies in Robustness integrate themes in fields like Minimum mean square error, Outlier and Adaptive filtering algorithm.
His Entropy research includes elements of Random variable, Artificial neural network, Mean squared error, Information theory and Applied mathematics.
His most cited work include:
- Quantized Kernel Least Mean Square Algorithm (303 citations)
- Similarity Learning with Spatial Constraints for Person Re-identification (295 citations)
- Generalized Correntropy for Robust Adaptive Filtering (268 citations)
What are the main themes of his work throughout his whole career to date?
Badong Chen focuses on Algorithm, Artificial intelligence, Robustness, Adaptive filter and Pattern recognition.
The concepts of his Algorithm study are interwoven with issues in Kernel, Mean squared error, Mathematical optimization, System identification and Entropy.
Badong Chen interconnects Machine learning and Computer vision in the investigation of issues within Artificial intelligence.
Cluster analysis and Optimization problem is closely connected to Outlier in his research, which is encompassed under the umbrella topic of Robustness.
Control theory covers Badong Chen research in Adaptive filter.
His Pattern recognition study integrates concerns from other disciplines, such as Decoding methods and Signal processing.
He most often published in these fields:
- Algorithm (48.46%)
- Artificial intelligence (26.33%)
- Robustness (23.53%)
What were the highlights of his more recent work (between 2018-2021)?
- Algorithm (48.46%)
- Artificial intelligence (26.33%)
- Robustness (23.53%)
In recent papers he was focusing on the following fields of study:
Badong Chen mostly deals with Algorithm, Artificial intelligence, Robustness, Pattern recognition and Outlier.
His biological study spans a wide range of topics, including Kernel, System identification, Kalman filter, Entropy and Nonlinear system.
The study incorporates disciplines such as Minimum mean square error and Signal processing in addition to Entropy.
His study in Robustness is interdisciplinary in nature, drawing from both Gradient descent, Linear programming, Machine learning and Gaussian noise.
His Pattern recognition research integrates issues from Subspace topology, Decoding methods, Feature and Electroencephalography.
The Outlier study combines topics in areas such as Point set registration, Filter, Random variable and Cluster analysis.
Between 2018 and 2021, his most popular works were:
- Blocked Maximum Correntropy Criterion Algorithm for Cluster-Sparse System Identifications (41 citations)
- Brain-Inspired Cognitive Model With Attention for Self-Driving Cars (37 citations)
- Robust rigid registration algorithm based on pointwise correspondence and correntropy (26 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Algorithm, Outlier, Robustness, Artificial intelligence and Kernel.
His Algorithm research includes themes of Kalman filter, Entropy, Noise measurement and Nonlinear system.
His Outlier study also includes fields such as
- Point set registration and related Point cloud and Iterative closest point,
- Norm, which have a strong connection to Mathematical optimization.
His Artificial intelligence study frequently involves adjacent topics like Pattern recognition.
Badong Chen combines subjects such as Filter, Convergence, System identification, Linear programming and Similarity measure with his study of Kernel.
His work carried out in the field of Compressed sensing brings together such families of science as Adaptive filter, Noise, Speedup and Convex optimization.
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