What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Yuan Yan Tang mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Wavelet transform.
His study in Image, Pixel, Facial recognition system, Image processing and Robustness are all subfields of Artificial intelligence.
In his research, Training set is intimately related to Regularization, which falls under the overarching field of Pattern recognition.
His work in the fields of Image segmentation, Feature detection and Segmentation overlaps with other areas such as Clutter.
His research integrates issues of Classifier, Feature, Principal component analysis and Support vector machine in his study of Feature extraction.
His Wavelet transform study which covers Edge detection that intersects with Structure, Multiresolution analysis and Image resolution.
His most cited work include:
- Face Recognition Under Varying Illumination Using Gradientfaces (334 citations)
- A Local Contrast Method for Small Infrared Target Detection (284 citations)
- High-order distance-based multiview stochastic learning in image classification. (228 citations)
What are the main themes of his work throughout his whole career to date?
Yuan Yan Tang spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Wavelet and Algorithm.
His research in Artificial intelligence tackles topics such as Machine learning which are related to areas like Data mining.
His Pattern recognition and Hyperspectral imaging, Sparse approximation, Dimensionality reduction, Feature vector and Support vector machine investigations all form part of his Pattern recognition research activities.
His studies in Image processing, Pixel and Image segmentation are all subfields of Computer vision research.
His Wavelet research includes themes of Speech recognition, Mathematical analysis and Pattern recognition.
His Signal processing research extends to the thematically linked field of Algorithm.
He most often published in these fields:
- Artificial intelligence (72.64%)
- Pattern recognition (52.15%)
- Computer vision (25.64%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (72.64%)
- Pattern recognition (52.15%)
- Algorithm (17.05%)
In recent papers he was focusing on the following fields of study:
Yuan Yan Tang mostly deals with Artificial intelligence, Pattern recognition, Algorithm, Computer vision and Hyperspectral imaging.
He works mostly in the field of Artificial intelligence, limiting it down to topics relating to Machine learning and, in certain cases, Data mining, as a part of the same area of interest.
His Pattern recognition study combines topics from a wide range of disciplines, such as Regression, Robustness and Cluster analysis.
Yuan Yan Tang mostly deals with Video tracking in his studies of Computer vision.
His Hyperspectral imaging research incorporates elements of Pixel, Image, Spatial analysis and Feature vector.
His Feature selection research is multidisciplinary, incorporating elements of Matrix and Dimensionality reduction.
Between 2016 and 2021, his most popular works were:
- Robust Object Tracking via Key Patch Sparse Representation (180 citations)
- Reliable asynchronous sampled-data filtering of T–S fuzzy uncertain delayed neural networks with stochastic switched topologies (170 citations)
- Nonfragile asynchronous control for uncertain chaotic Lurie network systems with Bernoulli stochastic process (145 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Sparse approximation are his primary areas of study.
Discriminative model, Feature extraction, Class, Support vector machine and Robustness are subfields of Artificial intelligence in which his conducts study.
Specifically, his work in Pattern recognition is concerned with the study of Hyperspectral imaging.
His studies in Computer vision integrate themes in fields like Salient, Simple, Ranking and Focus.
His work deals with themes such as Software quality, Data mining and Crowd psychology, which intersect with Machine learning.
His Sparse approximation research is multidisciplinary, relying on both Classifier, Facial recognition system, Matching pursuit and Outlier.
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.
