What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Pattern recognition
Wenming Zheng focuses on Artificial intelligence, Pattern recognition, Facial recognition system, Discriminative model and Feature extraction.
In his work, Feature selection is strongly intertwined with Speech recognition, which is a subfield of Artificial intelligence.
His work focuses on many connections between Pattern recognition and other disciplines, such as Computer vision, that overlap with his field of interest in Mixture model.
As part of the same scientific family, Wenming Zheng usually focuses on Facial recognition system, concentrating on Histogram and intersecting with Kernel, Image texture and Multiple kernel learning.
His study in Discriminative model is interdisciplinary in nature, drawing from both Adjacency matrix, Local binary patterns and Feature learning.
His research investigates the link between Feature extraction and topics such as Feature that cross with problems in Data set.
His most cited work include:
- Facial expression recognition using kernel canonical correlation analysis (KCCA) (211 citations)
- A Deep Neural Network-Driven Feature Learning Method for Multi-view Facial Expression Recognition (143 citations)
- Fisher Discriminant Analysis With L1-Norm (129 citations)
What are the main themes of his work throughout his whole career to date?
Wenming Zheng spends much of his time researching Artificial intelligence, Pattern recognition, Speech recognition, Feature extraction and Discriminative model.
His research on Artificial intelligence frequently connects to adjacent areas such as Computer vision.
His research investigates the connection between Pattern recognition and topics such as Face that intersect with problems in Image.
His work on Emotion recognition and Speaker recognition as part of general Speech recognition research is frequently linked to Novelty and Test data, bridging the gap between disciplines.
He focuses mostly in the field of Feature extraction, narrowing it down to matters related to Kernel and, in some cases, Kernel.
His Facial expression study combines topics in areas such as Feature learning and Convolutional neural network.
He most often published in these fields:
- Artificial intelligence (69.35%)
- Pattern recognition (59.68%)
- Speech recognition (23.12%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (69.35%)
- Pattern recognition (59.68%)
- Discriminative model (19.35%)
In recent papers he was focusing on the following fields of study:
Wenming Zheng mainly focuses on Artificial intelligence, Pattern recognition, Discriminative model, Emotion recognition and Focus.
His Artificial intelligence research includes elements of Machine learning and Brain–computer interface.
His studies deal with areas such as Facial recognition system and Microexpression as well as Machine learning.
His Pattern recognition study combines topics from a wide range of disciplines, such as Artificial neural network, Feature, Representation and Electroencephalography.
In his study, which falls under the umbrella issue of Discriminative model, Emotion classification and Feature selection is strongly linked to Feature extraction.
His work carried out in the field of Emotion recognition brings together such families of science as Eeg electrodes and Convolution.
Between 2019 and 2021, his most popular works were:
- EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks (105 citations)
- Feature Selection Based Transfer Subspace Learning for Speech Emotion Recognition (20 citations)
- Deep Manifold-to-Manifold Transforming Network for Skeleton-Based Action Recognition (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His main research concerns Artificial intelligence, Discriminative model, Feature extraction, Speech recognition and Convolutional neural network.
The study incorporates disciplines such as Machine learning and Pattern recognition in addition to Artificial intelligence.
The Machine learning study combines topics in areas such as Facial recognition system and Microexpression.
His Pattern recognition research integrates issues from Artificial neural network and Feature.
His studies in Discriminative model integrate themes in fields like Emotion classification and Feature selection.
Wenming Zheng combines subjects such as Mixture model, Deep learning, Theoretical computer science and Vertex with his study of Convolutional neural network.
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