What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Pattern recognition
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Hyperspectral imaging, Feature extraction and Computer vision.
His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Remote sensing.
He mostly deals with Discriminative model in his studies of Pattern recognition.
Lefei Zhang combines subjects such as Contextual image classification, Feature learning and Face with his study of Hyperspectral imaging.
His studies examine the connections between Deep learning and genetics, as well as such issues in Big data, with regards to Classifier.
His Pixel study incorporates themes from Matrix decomposition and Cluster analysis.
His most cited work include:
- Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art (849 citations)
- On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification (325 citations)
- Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction (240 citations)
What are the main themes of his work throughout his whole career to date?
Lefei Zhang mainly investigates Artificial intelligence, Pattern recognition, Hyperspectral imaging, Computer vision and Discriminative model.
He studied Artificial intelligence and Machine learning that intersect with Classifier.
His Pattern recognition study integrates concerns from other disciplines, such as Embedding and Cluster analysis.
His Hyperspectral imaging research incorporates themes from Pixel, Spatial analysis and Support vector machine.
His work carried out in the field of Discriminative model brings together such families of science as Data mining, Outlier and Hyperspectral image classification.
His work investigates the relationship between Feature extraction and topics such as Kernel that intersect with problems in Kernel and Algorithm.
He most often published in these fields:
- Artificial intelligence (85.21%)
- Pattern recognition (61.27%)
- Hyperspectral imaging (40.14%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (85.21%)
- Pattern recognition (61.27%)
- Feature (17.61%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Feature, Image and Inpainting.
The study incorporates disciplines such as Machine learning and Computer vision in addition to Artificial intelligence.
His Pattern recognition research includes themes of Subspace topology and Deep learning.
His biological study spans a wide range of topics, including Pyramid, Pixel and Pyramid.
His research in Feature intersects with topics in Matching, Active learning and Kernel.
Lefei Zhang works mostly in the field of Hyperspectral imaging, limiting it down to topics relating to Principal component analysis and, in certain cases, Subpixel rendering.
Between 2019 and 2021, his most popular works were:
- Unsupervised Domain Adaptive Re-Identification: Theory and Practice (89 citations)
- Dimensionality Reduction With Enhanced Hybrid-Graph Discriminant Learning for Hyperspectral Image Classification (45 citations)
- Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Feature, Machine learning and Image.
His Support vector machine, Feature extraction and Convolutional neural network study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Encoder and Context, bridging the gap between disciplines.
His Feature extraction research includes elements of Hyperspectral imaging, Kernel, Kernel and Hyperspectral image classification.
He has researched Hyperspectral imaging in several fields, including Embedding, Discriminant, Principal component analysis and Dimensionality reduction.
In most of his Pattern recognition studies, his work intersects topics such as Data set.
His study in the fields of Re identification and Feature vector under the domain of Machine learning overlaps with other disciplines such as Domain, Code and Scheme.
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