One-step unsupervised clustering based on information theoretic metric and adaptive neighbor manifold regularization-Reference-Cited by-同舟云学术

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One-step unsupervised clustering based on information theoretic metric and adaptive neighbor manifold regularization

Published:2023-04 Issue: Volume:120 Page:105880
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Li Xinyu,Fan Hui,Liu Jinglei

Funder

Yantai University

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference47 articles.

1. Discriminative nonnegative matrix factorization for dimensionality reduction;Babaee;Neurocomputing,2016

2. Graph regularized nonnegative matrix factorization for data representation;Cai;IEEE Trans. Pattern Anal. Mach. Intell.,2011

3. Unsupervised novelty detection–based structural damage localization using a density peaks-based fast clustering algorithm;Cha;Struct. Health Monit.,2018

4. A weight-adaptive Laplacian embedding for graph-based clustering;Cheng;Neural Comput.,2017

5. Dhillon, I.S., 2001. Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 269–274.

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1. Dynamic community detection algorithm based on hyperbolic graph convolution;International Journal of Intelligent Computing and Cybernetics;2024-07-04

2. Scalable incomplete multi-view clustering via tensor Schatten p-norm and tensorized bipartite graph;Engineering Applications of Artificial Intelligence;2023-08

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