What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Feiping Nie mainly investigates Artificial intelligence, Pattern recognition, Cluster analysis, Machine learning and Dimensionality reduction.
His research in Artificial intelligence intersects with topics in Graph and Data mining.
He has included themes like Contextual image classification, Data point and Image retrieval in his Pattern recognition study.
His biological study spans a wide range of topics, including Laplacian matrix and Graph.
His Machine learning research is multidisciplinary, incorporating elements of Kernelization and Pairwise comparison.
His Dimensionality reduction study integrates concerns from other disciplines, such as Curse of dimensionality, Embedding, Linear discriminant analysis, Mathematical optimization and Algorithm.
His most cited work include:
- Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization (1178 citations)
- Learning a Mahalanobis distance metric for data clustering and classification (391 citations)
- A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback (353 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, Cluster analysis, Algorithm and Graph.
In his work, Training set is strongly intertwined with Machine learning, which is a subfield of Artificial intelligence.
His Pattern recognition research focuses on Outlier and how it connects with Minification.
His research is interdisciplinary, bridging the disciplines of Data mining and Cluster analysis.
His research in Algorithm intersects with topics in Matrix decomposition, Matrix, Embedding and Mathematical optimization.
His Graph research incorporates elements of Computational complexity theory, Theoretical computer science and Laplacian matrix, Graph.
He most often published in these fields:
- Artificial intelligence (58.77%)
- Pattern recognition (41.79%)
- Cluster analysis (30.04%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (58.77%)
- Pattern recognition (41.79%)
- Cluster analysis (30.04%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Cluster analysis, Algorithm and Graph.
Discriminative model, Feature selection, Dimensionality reduction, Feature extraction and Linear discriminant analysis are the primary areas of interest in his Artificial intelligence study.
Feiping Nie focuses mostly in the field of Pattern recognition, narrowing it down to matters related to Iterative method and, in some cases, Support vector machine.
His Cluster analysis research includes themes of Embedding, Matrix, Data mining and Data set.
Feiping Nie has included themes like Matrix decomposition, Spectral clustering, Outlier and Laplace operator in his Algorithm study.
The study incorporates disciplines such as Semi-supervised learning, Data modeling, Theoretical computer science and Laplacian matrix, Graph in addition to Graph.
Between 2019 and 2021, his most popular works were:
- Detecting Coherent Groups in Crowd Scenes by Multiview Clustering (143 citations)
- Fast spectral clustering learning with hierarchical bipartite graph for large-scale data (21 citations)
- Multi-view spectral clustering via integrating nonnegative embedding and spectral embedding (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Feiping Nie spends much of his time researching Artificial intelligence, Pattern recognition, Cluster analysis, Feature extraction and Graph.
As part of the same scientific family, Feiping Nie usually focuses on Artificial intelligence, concentrating on Machine learning and intersecting with Training set.
He combines subjects such as Decision tree and Feature with his study of Pattern recognition.
His Cluster analysis research integrates issues from Algorithm, Matrix and Data set.
His Feature extraction research includes elements of Feature selection, Contextual image classification, Iterative method, Unsupervised learning and Iterative reconstruction.
His Graph study combines topics in areas such as Computational complexity theory, Data modeling, Similarity matrix and Graph.
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