Unsupervised Cognition

• URL: http://arxiv.org/abs/2409.18624v2
• Date: Thu, 07 Nov 2024 14:36:04 GMT
• Title: Unsupervised Cognition
• Authors: Alfredo Ibias, Hector Antona, Guillem Ramirez-Miranda, Enric Guinovart, Eduard Alarcon,
• Abstract summary: We propose a primitive-based, unsupervised learning approach for decision-making inspired by a novel cognition framework. This representation-centric approach models the input space constructively as a distributed hierarchical structure in an input-agnostic way. We show how our proposal outperforms previous state-of-the-art unsupervised learning classification.
• Score: 2.5069344340760713
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 a novel cognition framework. This representation-centric approach models the input space constructively as a distributed hierarchical structure in an input-agnostic way. We compared our approach with both current state-of-the-art unsupervised learning classification, and with current state-of-the-art 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.

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