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Found 3 papers, 2 papers with code
Gradient-based Competitive Learning: Theory
The former is just an adaptation of a standard competitive layer for deep clustering, while the latter is trained on the transposed matrix.
Topological Gradient-based Competitive Learning
The aim of this work is to present a novel comprehensive theory aspiring at bridging competitive learning with gradient-based learning, thus allowing the use of extremely powerful deep neural networks for feature extraction and projection combined with the remarkable flexibility and expressiveness of competitive learning.
The GH-EXIN neural network for hierarchical clustering
Furthermore, an important and very promising application of GH-EXIN in two-way hierarchical clustering, for the analysis of gene expression data in the study of the colorectal cancer is described.
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