What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Pattern recognition
His primary scientific interests are in Artificial intelligence, Pattern recognition, Linear discriminant analysis, Dimensionality reduction and Subspace topology.
His research is interdisciplinary, bridging the disciplines of Machine learning and Artificial intelligence.
His Pattern recognition research includes elements of Facial recognition system, Data point, Cluster analysis and Manifold.
His work carried out in the field of Linear discriminant analysis brings together such families of science as Regression analysis and Regularization.
The study incorporates disciplines such as Optimization problem and Discriminative model in addition to Dimensionality reduction.
He has included themes like Principal component analysis and Projection in his Subspace topology study.
His most cited work include:
- Locality Preserving Projections (3394 citations)
- Face recognition using Laplacianfaces (2852 citations)
- Graph Regularized Nonnegative Matrix Factorization for Data Representation (1506 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Pattern recognition, Machine learning, Information retrieval and Dimensionality reduction.
His research on Artificial intelligence frequently connects to adjacent areas such as Computer vision.
The various areas that Xiaofei He examines in his Pattern recognition study include Facial recognition system and Data mining.
His Information retrieval study incorporates themes from Graph and Web page.
His Dimensionality reduction research incorporates themes from Unsupervised learning, Latent semantic indexing, Curse of dimensionality and Feature vector.
The Subspace topology study combines topics in areas such as Embedding and Projection.
He most often published in these fields:
- Artificial intelligence (61.73%)
- Pattern recognition (37.45%)
- Machine learning (20.99%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (61.73%)
- Machine learning (20.99%)
- Natural language processing (4.12%)
In recent papers he was focusing on the following fields of study:
Xiaofei He spends much of his time researching Artificial intelligence, Machine learning, Natural language processing, Pattern recognition and Information retrieval.
His work is connected to Feature extraction, Benchmark, Feature, Discriminative model and Representation, as a part of Artificial intelligence.
His work on Feature learning, Re identification and Active learning as part of general Machine learning research is frequently linked to Random walk, bridging the gap between disciplines.
He combines Pattern recognition and Focus in his research.
His Question answering and Ranking study in the realm of Information retrieval interacts with subjects such as Encoder, Social influence and Beta distribution.
His study in Cluster analysis is interdisciplinary in nature, drawing from both Nonlinear dimensionality reduction, Manifold and Subspace topology.
Between 2016 and 2021, his most popular works were:
- Robust Structured Nonnegative Matrix Factorization for Image Representation (115 citations)
- Weakly-Supervised Deep Embedding for Product Review Sentiment Analysis (63 citations)
- Video Question Answering via Hierarchical Spatio-Temporal Attention Networks (62 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Xiaofei He mainly focuses on Artificial intelligence, Natural language processing, Information retrieval, Question answering and Theoretical computer science.
Many of his studies on Artificial intelligence apply to Machine learning as well.
Xiaofei He interconnects Object and DUAL in the investigation of issues within Information retrieval.
His work deals with themes such as Relation, Graph, Knowledge graph and Graph, which intersect with Theoretical computer science.
His study explores the link between Training set and topics such as Face that cross with problems in Pattern recognition.
His Pattern recognition research focuses on Optimization problem and how it connects with Robustness.
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