What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of investigation include Artificial intelligence, Information retrieval, Pattern recognition, Machine learning and Image retrieval.
His research links Graph with Artificial intelligence.
His study in the field of Relevance, Ranking and Query expansion also crosses realms of Set.
His work on Sparse approximation and Hidden Markov model as part of general Pattern recognition research is frequently linked to Locality-sensitive hashing and K-independent hashing, bridging the gap between disciplines.
His work on Support vector machine as part of general Machine learning study is frequently connected to Space, TRECVID and Disease progression, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Image retrieval research integrates issues from Similitude and Search engine indexing.
His most cited work include:
- Tag ranking (456 citations)
- 3-D Object Retrieval and Recognition With Hypergraph Analysis (453 citations)
- Unified Video Annotation via Multigraph Learning (412 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Machine learning, Information retrieval and Image.
His research ties Computer vision and Artificial intelligence together.
Meng Wang usually deals with Pattern recognition and limits it to topics linked to Robustness and Factorization.
The study incorporates disciplines such as Representation, Data mining and Metric in addition to Machine learning.
In the subject of general Information retrieval, his work in Ranking and Relevance is often linked to Scheme and Set, thereby combining diverse domains of study.
Meng Wang focuses mostly in the field of Feature extraction, narrowing it down to matters related to Visualization and, in some cases, Image retrieval.
He most often published in these fields:
- Artificial intelligence (64.82%)
- Pattern recognition (31.13%)
- Machine learning (21.54%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (64.82%)
- Pattern recognition (31.13%)
- Machine learning (21.54%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Pattern recognition, Machine learning, Graph and Image are his primary areas of study.
Artificial intelligence is frequently linked to Natural language processing in his study.
His work deals with themes such as Subspace topology, Cluster analysis, Salient and Manifold, which intersect with Pattern recognition.
He combines subjects such as Embedding, Similarity, Training set and Metric with his study of Machine learning.
His Graph research is multidisciplinary, incorporating perspectives in Theoretical computer science, Artificial neural network, Recommender system, Structure and Graph.
Meng Wang interconnects Visualization and Benchmark in the investigation of issues within Image.
Between 2019 and 2021, his most popular works were:
- LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation (120 citations)
- Generative Adversarial Active Learning for Unsupervised Outlier Detection (65 citations)
- Joint Label Prediction Based Semi-Supervised Adaptive Concept Factorization for Robust Data Representation (48 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Meng Wang focuses on Artificial intelligence, Pattern recognition, Discriminative model, Robustness and Image.
His Artificial intelligence research includes themes of Machine learning and Computer vision.
His Machine learning study integrates concerns from other disciplines, such as Embedding and Representation.
His Pattern recognition research is multidisciplinary, relying on both Salient, Outlier, Feature and Manifold.
As part of the same scientific family, Meng Wang usually focuses on Discriminative model, concentrating on Similarity learning and intersecting with Relevance, Document retrieval, Similarity and Disjoint sets.
His Robustness research incorporates elements of Algorithm and Sparse approximation.
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