Anisotropic-Gaussian-Kernel-Based Fuzzy Clustering Algorithm for Feature Selection

Soft clustering algorithms based on fuzzy C-means (FCM) have been extensively applied to complex data analysis. However, existing FCM variants still encounter key limitations: a large number of iterations due to slow convergence on high-dimensional data, equal weights of all samples, which increases...

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Vydáno v:	IEEE transactions on fuzzy systems Ročník 33; číslo 9; s. 3061 - 3075
Hlavní autoři:	Liu, Jun, Wu, Mengyuan, Lin, Mingwei, Xu, Zeshui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	IEEE 01.09.2025
Témata:	Accuracy Adaptive weights Anisotropic anisotropic Gaussian kernel (AGK) Classification algorithms Clustering algorithms Feature extraction feature reduction fuzzy C-means (FCM) Kernel Linear programming Machine learning algorithms Noise particle swarm optimization (PSO) algorithm with time-varying acceleration coefficients (PSO-TVAC) Vectors
ISSN:	1063-6706, 1941-0034
On-line přístup:	Získat plný text
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