An Enhanced Regularized Clustering Method With Adaptive Spurious Connection Detection

The regularized clustering (RC) framework based on the fusion penalty has attracted extensive attention in the last decade because it does not require the prior knowledge of the number of clusters. Although the ground truth connections and weights among samples are beneficial for clustering, the per...

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Vydané v:IEEE signal processing letters Ročník 30; s. 1332 - 1336
Hlavní autori: Chen, Huangyue, Kong, Lingchen, Qu, Wentao, Xiu, Xianchao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Alternating minimization algorithm
Clustering
Clustering algorithms
Convergence
fusion penalty
Linear programming
Minimization
Monitoring
Optimization
regularized clustering
Signal processing algorithms
spurious connections
ISSN:1070-9908, 1558-2361
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Shrnutí:The regularized clustering (RC) framework based on the fusion penalty has attracted extensive attention in the last decade because it does not require the prior knowledge of the number of clusters. Although the ground truth connections and weights among samples are beneficial for clustering, the performance of RC could be distorted by the omnipresent spurious connections in real-world data sets. To effectively address the issue, this letter constructs an enhanced regularized clustering model by incorporating a spurious connection detection mechanism into the objective function of the RC framework. The proposed model can effectively reduce the damage caused by spurious connections through adaptively identifying the importance of each connection. Furthermore, an alternating minimization algorithm is developed with detailed convergence analysis. Experimental results validate its effectiveness against several state-of-the-art RC methods.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2023.3316023