What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Pattern recognition, Cluster analysis, Machine learning and Data mining.
Artificial intelligence is a component of his Feature, Feature selection, Feature extraction, Dimensionality reduction and Contextual image classification studies.
His work deals with themes such as Regularization, Outlier and Subspace topology, which intersect with Pattern recognition.
His Cluster analysis study incorporates themes from Algorithm and Non-negative matrix factorization.
His Machine learning study combines topics in areas such as Cognition, Neuroimaging and Automatic image annotation.
His Data mining research incorporates elements of Data point, Feature, Feature learning and k-means clustering.
His most cited work include:
- Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization (1178 citations)
- Clustering and projected clustering with adaptive neighbors (336 citations)
- Multi-view K-means clustering on big data (295 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Machine learning, Algorithm and Cluster analysis.
Heng Huang interconnects Data mining and Computer vision in the investigation of issues within Artificial intelligence.
His study in Contextual image classification extends to Pattern recognition with its themes.
His Machine learning research integrates issues from Graph, Neuroimaging and Training set.
His work carried out in the field of Algorithm brings together such families of science as Metric and Benchmark.
His work in Cluster analysis covers topics such as Mathematical optimization which are related to areas like Stochastic gradient descent and Rate of convergence.
He most often published in these fields:
- Artificial intelligence (54.87%)
- Pattern recognition (27.33%)
- Machine learning (23.31%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (54.87%)
- Algorithm (16.95%)
- Deep learning (7.20%)
In recent papers he was focusing on the following fields of study:
Heng Huang focuses on Artificial intelligence, Algorithm, Deep learning, Benchmark and Machine learning.
In his work, Domain is strongly intertwined with Pattern recognition, which is a subfield of Artificial intelligence.
His research investigates the connection between Algorithm and topics such as Kernel that intersect with problems in Kernel method, Kernel and Cluster analysis.
Heng Huang has researched Deep learning in several fields, including Eye disease, Age related, Image and Computer engineering.
His research integrates issues of Discrete mathematics, Applied mathematics, Rate of convergence, Generalization and Stationary point in his study of Benchmark.
His work on Labeled data and Support vector machine as part of his general Machine learning study is frequently connected to Focus, thereby bridging the divide between different branches of science.
Between 2019 and 2021, his most popular works were:
- 3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images (28 citations)
- Deep-learning-based prediction of late age-related macular degeneration progression (16 citations)
- BREM-SC: a bayesian random effects mixture model for joint clustering single cell multi-omics data. (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Artificial intelligence, Algorithm, Benchmark, Segmentation and Rate of convergence are his primary areas of study.
Artificial intelligence is closely attributed to Machine learning in his research.
His Algorithm research integrates issues from Current, Information privacy and Feature learning.
The study incorporates disciplines such as Perspective, Estimator and Theoretical computer science in addition to Benchmark.
Segmentation is a subfield of Pattern recognition that Heng Huang investigates.
The concepts of his Pattern recognition study are interwoven with issues in Margin and Feature.
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