Unsupervised Cognition
<|reference_start|>Unsupervised Cognition: Unsupervised learning methods have a soft inspiration in cognition models. To this day, the most successful unsupervised learning methods revolve around clustering samples in a mathematical space. In this paper we propose a state-of-the-art primitive-based unsupervised learning approach for decision-making inspired by novel cognition models. This representation-centric approach models the input space constructively as a distributed hierarchical structure in an input-agnostic way. We compared our approach with current state-of-the-art in unsupervised learning classification, and with current state-of-the-art in cancer type classification. We show how our proposal outperforms previous state-of-the-art. We also evaluate some cognition-like properties of our proposal where it not only outperforms the compared algorithms (even supervised learning ones), but it also shows a different, more cognition-like, behaviour.<|reference_end|>
@article{ibias2024unsupervised,
    title={Unsupervised Cognition},
    author={Alfredo Ibias, Hector Antona, Guillem Ramirez-Miranda, Enric
  Guinovart, Eduard Alarcon},
    journal={arXiv preprint arXiv:2409.18624},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18624},
    primaryClass={cs.AI cs.LG}
}