Hypergraph based semi-supervised symmetric nonnegative matrix factorization for image clustering-Reference-Cited by-同舟云学术

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Hypergraph based semi-supervised symmetric nonnegative matrix factorization for image clustering

Published:2023-05 Issue: Volume:137 Page:109274
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Yin Jingxing,Peng Siyuan^ORCID,Yang Zhijing,Chen Badong,Lin Zhiping

Funder

Guangzhou Municipal Science and Technology Project

Science and Technology Planning Project of Guangdong Province

Basic and Applied Basic Research Foundation of Guangdong Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference36 articles.

1. Data Clustering: Theory, Algorithms, and Applications;Gan,2020

2. Auto-weighted multi-view co-clustering via fast matrix factorization;Nie;Pattern Recognit.,2020

3. Self-weighting multi-view spectral clustering based on nuclear norm;Shi;Pattern Recognit.,2022

4. Systematic review of clustering high-dimensional and large datasets;Pandove;ACM Trans. Knowl. Discov. Data (TKDD),2018

5. Robust orthogonal nonnegative matrix tri-factorization for data representation;Peng;Knowl. Based Syst.,2020

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1. Sparse feature selection using hypergraph Laplacian-based semi-supervised discriminant analysis;Pattern Recognition;2025-01

2. Semi-supervised pivotal-aware nonnegative matrix factorization with label and pairwise constraint propagation for data clustering;Pattern Recognition;2025-01

3. A simple multi-constraint fusion based semi-supervised non-negative matrix decomposition for image clustering;Neurocomputing;2024-12

4. Hypergraph-based convex semi-supervised unconstraint symmetric matrix factorization for image clustering;Information Sciences;2024-10

5. Class-driven nonnegative matrix factorization with manifold regularization for data clustering;Neurocomputing;2024-08

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