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Feiping Nie
Researcher at Northwestern Polytechnical University
Publications - 668
Citations - 31604
Feiping Nie is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Cluster analysis & Computer science. The author has an hindex of 79, co-authored 541 publications receiving 23016 citations. Previous affiliations of Feiping Nie include Nanyang Technological University & Tsinghua University.
Papers
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Proceedings Article
Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization
TL;DR: A new robust feature selection method with emphasizing joint l2,1-norm minimization on both loss function and regularization is proposed, which has been applied into both genomic and proteomic biomarkers discovery.
Proceedings ArticleDOI
Clustering and projected clustering with adaptive neighbors
TL;DR: This paper proposes a novel clustering model to learn the data similarity matrix and clustering structure simultaneously and derives an efficient algorithm to optimize the proposed challenging problem, and shows the theoretical analysis on the connections between the method and the K-means clustering, and spectral clustering.
Proceedings Article
The Constrained Laplacian Rank algorithm for graph-based clustering
TL;DR: This work develops two versions of the Constrained Laplacian Rank (CLR) method, based upon the L1-norm and the L2-norm, which yield two new graph-based clustering objectives and derives optimization algorithms to solve them.
Journal ArticleDOI
Learning a Mahalanobis distance metric for data clustering and classification
TL;DR: This paper considers a general problem of learning from pairwise constraints in the form of must-links and cannot-links, and aims to learn a Mahalanobis distance metric.
Proceedings Article
Multi-view K-means clustering on big data
Xiao Cai,Feiping Nie,Heng Huang +2 more
TL;DR: This paper proposes a new robust large-scale multi-view clustering method to integrate heterogeneous representations of largescale data and evaluates the proposed new methods by six benchmark data sets and compared the performance with several commonly used clustering approaches as well as the baseline multi- view clustering methods.