2023 - Research.com Computer Science in China Leader Award
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.
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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization
Feiping Nie;Heng Huang;Xiao Cai;Chris H. Ding.
neural information processing systems (2010)
Learning a Mahalanobis distance metric for data clustering and classification
Shiming Xiang;Feiping Nie;Changshui Zhang.
Pattern Recognition (2008)
Clustering and projected clustering with adaptive neighbors
Feiping Nie;Xiaoqian Wang;Heng Huang.
knowledge discovery and data mining (2014)
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
Feiping Nie;Dong Xu;Ivor Wai-Hung Tsang;Changshui Zhang.
IEEE Transactions on Image Processing (2010)
Multi-view K-means clustering on big data
Xiao Cai;Feiping Nie;Heng Huang.
international joint conference on artificial intelligence (2013)
Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection
Chenping Hou;Feiping Nie;Xuelong Li;Dongyun Yi.
IEEE Transactions on Systems, Man, and Cybernetics (2014)
A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback
Yi Yang;Feiping Nie;Dong Xu;Jiebo Luo.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)
The Constrained Laplacian Rank algorithm for graph-based clustering
Feiping Nie;Xiaoqian Wang;Michael I. Jordan;Heng Huang.
national conference on artificial intelligence (2016)
Trace ratio criterion for feature selection
Feiping Nie;Shiming Xiang;Yangqing Jia;Changshui Zhang.
national conference on artificial intelligence (2008)
Discriminative Least Squares Regression for Multiclass Classification and Feature Selection
Shiming Xiang;Feiping Nie;Gaofeng Meng;Chunhong Pan.
IEEE Transactions on Neural Networks (2012)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: