What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Yong Xu focuses on Artificial intelligence, Pattern recognition, Facial recognition system, Feature extraction and Sparse approximation.
He has included themes like Algorithm, Regression analysis, Data mining and Computer vision in his Artificial intelligence study.
His Pattern recognition study frequently draws parallels with other fields, such as Contextual image classification.
His work investigates the relationship between Facial recognition system and topics such as k-nearest neighbors algorithm that intersect with problems in Nearest neighbor search.
His studies in Feature extraction integrate themes in fields like Biometrics, Subspace topology and Nonlinear dimensionality reduction, Dimensionality reduction.
His biological study spans a wide range of topics, including Pixel, Rate of convergence, Sparse matrix and Robust regression.
His most cited work include:
- A Survey of Sparse Representation: Algorithms and Applications (595 citations)
- A Two-Phase Test Sample Sparse Representation Method for Use With Face Recognition (431 citations)
- Image retrieval based on micro-structure descriptor (233 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Pattern recognition, Feature extraction, Facial recognition system and Computer vision.
His study in Discriminative model, Pattern recognition, Face, Biometrics and Image are all subfields of Artificial intelligence.
His Pattern recognition research incorporates elements of Pixel and Feature.
His Feature extraction study combines topics from a wide range of disciplines, such as Subspace topology, Data mining, Projection, Feature vector and Dimensionality reduction.
His Facial recognition system research is multidisciplinary, relying on both Algorithm, Sample, Representation and k-nearest neighbors algorithm.
His Linear discriminant analysis research is multidisciplinary, incorporating perspectives in Kernel Fisher discriminant analysis and Discriminant.
He most often published in these fields:
- Artificial intelligence (87.29%)
- Pattern recognition (72.91%)
- Feature extraction (27.76%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (87.29%)
- Pattern recognition (72.91%)
- Convolutional neural network (9.03%)
In recent papers he was focusing on the following fields of study:
Yong Xu spends much of his time researching Artificial intelligence, Pattern recognition, Convolutional neural network, Image and Block.
His work is connected to Feature extraction, Deep learning, Feature, Cluster analysis and Convolution, as a part of Artificial intelligence.
His research investigates the connection between Pattern recognition and topics such as Contextual image classification that intersect with issues in Sparse approximation.
His Convolutional neural network study integrates concerns from other disciplines, such as Noise, Noise reduction and Code.
His study looks at the relationship between Image and fields such as Algorithm, as well as how they intersect with chemical problems.
His work deals with themes such as Discriminant and Feature learning, which intersect with Discriminative model.
Between 2019 and 2021, his most popular works were:
- Image denoising using deep CNN with batch renormalization. (77 citations)
- Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (55 citations)
- Attention-guided CNN for image denoising. (54 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Convolutional neural network, Image and Feature extraction.
His studies deal with areas such as Sequence and Computer vision as well as Artificial intelligence.
His Image processing study, which is part of a larger body of work in Computer vision, is frequently linked to Boundary, bridging the gap between disciplines.
His Pattern recognition research incorporates themes from Matrix decomposition and Cluster analysis.
His work carried out in the field of Convolutional neural network brings together such families of science as Noise reduction, Image fusion, Regression and Code.
The concepts of his Feature extraction study are interwoven with issues in Filter, DUAL, Tree, Compression and Discriminative model.
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