Improving projected fuzzy K-means clustering via robust learning-Reference-Cited by-同舟云学术

Improving projected fuzzy K-means clustering via robust learning

Published:2022-06 Issue: Volume:491 Page:34-43
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Zhao Xiaowei,Nie Feiping,Wang Rong,Li Xuelong

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Natural Science Basic Research Program of Shaanxi Province

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference48 articles.

1. Self-weighted robust lda for multiclass classification with edge classes;Yan;ACM Transactions on Intelligent Systems and Technology (TIST),2020

2. X. Lu, L. Liu, L. Nie, X. Chang, H. Zhang, Semantic-driven interpretable deep multi-modal hashing for large-scale multimedia retrieval, IEEE Transactions on Multimedia.

3. Normalized cuts and image segmentation;Shi;IEEE Transactions on Pattern Analysis and Machine Intelligence,2000

4. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters, Journal of;Dunn;Cybernetics,1973

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