SOM-OLP introduces per-data-point continuous latent positions and a separable quadratic surrogate objective derived from STVQ, yielding efficient monotonic block coordinate descent with linear complexity and competitive performance on benchmarks.
Self-organized formation of topologically correct feature maps,
2 Pith papers cite this work. Polarity classification is still indexing.
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An ART-based topological clustering algorithm estimates vigilance and edge deletion thresholds automatically using determinantal point process and edge age for parameter-free continual learning.
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Self-Organizing Maps with Optimized Latent Positions
SOM-OLP introduces per-data-point continuous latent positions and a separable quadratic surrogate objective derived from STVQ, yielding efficient monotonic block coordinate descent with linear complexity and competitive performance on benchmarks.
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A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning
An ART-based topological clustering algorithm estimates vigilance and edge deletion thresholds automatically using determinantal point process and edge age for parameter-free continual learning.