Clustering properties of hierarchical self-organizing maps

DOI10.1007/BF00118594zbMath0790.92002DBLPjournals/jmiv/LampinenO92OpenAlexW4247446713WikidataQ50376974 ScholiaQ50376974MaRDI QIDQ1318601

Jouko Lampinen, Erkki Oja

Publication date: 7 April 1994

Published in: Journal of Mathematical Imaging and Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00118594

zbMATH Keywords

image analysismultilayer feedforward networksHSOMmultilayer hierarchical self-organizing mapunsupervised clustering method

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (7)

Optimization approach for clustering datasets with weights ⋮ Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density. ⋮ 3D head model retrieval in kernel feature space using HSOM ⋮ Pattern classification using multiple hierarchical overlapped self-organising maps ⋮ Smoothly distributed fuzzy \(c\)-means: A new self-organizing map ⋮ On the equivalence between kernel self-organising maps and self-organising mixture density networks ⋮ A NOVEL NEUTROSOPHIC LOGIC SVM (N-SVM) AND ITS APPLICATION TO IMAGE CATEGORIZATION

Cites Work

• Unnamed Item
• Self-organization and associative memory
• Self-organized formation of topologically correct feature maps
• Topographic organization of nerve fields
• Multilayer feedforward networks are universal approximators
• Image compression using a multilayer neural network

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