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arXiv · 2503.07641

Deep ARTMAP: Generalized Hierarchical Learning with Adaptive Resonance Theory

Niklas M. Melton·Leonardo Enzo Brito da Silva·Sasha Petrenko·Donald. C. Wunsch II
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Abstract

This paper presents Deep ARTMAP, a novel extension of the ARTMAP architecture that generalizes the self-consistent modular ART (SMART) architecture to enable hierarchical learning (supervised and unsupervised) across arbitrary transformations of data. The Deep ARTMAP framework operates as a divisive clustering mechanism, supporting an arbitrary number of modules with customizable granularity within each module. Inter-ART modules regulate the clustering at each layer, permitting unsupervised learning while enforcing a one-to-many mapping from clusters in one layer to the next. While Deep ARTMAP reduces to both ARTMAP and SMART in particular configurations, it offers significantly enhanced flexibility, accommodating a broader range of data transformations and learning modalities.

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Niklas M. Melton, Leonardo Enzo Brito da Silva, Sasha Petrenko, Donald. C. Wunsch II. 2025-03-05. Deep ARTMAP: Generalized Hierarchical Learning with Adaptive Resonance Theory. https://arxiv.org/abs/2503.07641

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