Fast density-weighted low-rank approximation spectral clustering

DOI10.1007/s10618-010-0207-5zbMath1235.68187OpenAlexW1978373800MaRDI QIDQ408661

Jiarong Shi, Maoguo Gong, Fanhua Shang, Ronghua Shang, Li-Cheng Jiao

Publication date: 11 April 2012

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-010-0207-5

zbMATH Keywords

clusteringdata miningNyström approximationspectral clusteringaffinity propagationmanifold structure

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

Graph dual regularization non-negative matrix factorization for co-clustering ⋮ Fast semi-supervised clustering with enhanced spectral embedding ⋮ An efficient matrix bi-factorization alternative optimization method for low-rank matrix recovery and completion ⋮ Effective implementation to reduce execution time of a low-rank matrix approximation problem

Uses Software

• apcluster
• APCluster
• MNIST
• Isomap

Cites Work

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• Clustering by Passing Messages Between Data Points
• Data mining. Concepts and techniques
• Likelihood-based data squashing: A modeling approach to instance construction
• Extensions of Lipschitz mappings into a Hilbert space
• Density-Weighted Nyström Method for Computing Large Kernel Eigensystems
• Factor graphs and the sum-product algorithm
• 10.1162/153244303321897735
• Lower Bounds for the Partitioning of Graphs

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