What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of investigation include Artificial intelligence, Algorithm, Pattern recognition, Adjacency matrix and Remote sensing.
The various areas that Bin Luo examines in his Artificial intelligence study include Machine learning and Computer vision.
He has included themes like Parallelism and Real image in his Algorithm study.
His Adjacency matrix study combines topics in areas such as Graph theory, Graph and 3-dimensional matching.
The Combinatorics study which covers Discrete mathematics that intersects with Adjacency list.
He combines subjects such as Embedding and Multidimensional scaling with his study of Eigenvalues and eigenvectors.
His most cited work include:
- Structural graph matching using the EM algorithm and singular value decomposition (310 citations)
- Spectral embedding of graphs (228 citations)
- Spectral embedding of graphs (228 citations)
What are the main themes of his work throughout his whole career to date?
Bin Luo spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Feature.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Graph.
His work on Convolutional neural network, Image segmentation, Dimensionality reduction and Mixture model as part of general Pattern recognition study is frequently connected to Expectation–maximization algorithm, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
The study incorporates disciplines such as Information hiding and Embedding in addition to Algorithm.
His Discriminative model research extends to the thematically linked field of Feature.
His biological study spans a wide range of topics, including Hyperspectral imaging and Remote sensing.
He most often published in these fields:
- Artificial intelligence (67.44%)
- Pattern recognition (41.86%)
- Computer vision (23.02%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (67.44%)
- Pattern recognition (41.86%)
- Computer vision (23.02%)
In recent papers he was focusing on the following fields of study:
Bin Luo mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Feature.
His Artificial intelligence study frequently intersects with other fields, such as Graph.
His studies in Pattern recognition integrate themes in fields like Focus, Convolution, Residual and Cluster analysis.
In his work, Reference frame and Motion is strongly intertwined with Trajectory, which is a subfield of Computer vision.
In his research on the topic of Convolutional neural network, Feature aggregation is strongly related with Pooling.
As a part of the same scientific study, Bin Luo usually deals with the Feature, concentrating on Discriminative model and frequently concerns with Matching.
Between 2018 and 2021, his most popular works were:
- Semi-Supervised Learning With Graph Learning-Convolutional Networks (61 citations)
- Dense Feature Aggregation and Pruning for RGBT Tracking (17 citations)
- Image Representation and Learning With Graph-Laplacian Tucker Tensor Decomposition (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Graph and Convolutional neural network.
His work on Artificial intelligence deals in particular with Deep learning, Feature extraction, Feature learning, Segmentation and RGB color model.
Bin Luo interconnects Optimal estimation, Normalization, Multispectral image and Cluster analysis in the investigation of issues within Pattern recognition.
His Computer vision study incorporates themes from Visualization, Dense graph, Structured support vector machine and Benchmark.
His Graph research incorporates elements of Video tracking, Embedding, Graph and Theoretical computer science.
His study in Convolutional neural network is interdisciplinary in nature, drawing from both Confocal, Microscope and Position.
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