Unsupervised Cognition
This addresses the need for more cognition-like unsupervised learning methods in domains such as healthcare, though it appears incremental as it builds on existing paradigms.
The paper tackles the problem of unsupervised learning for decision-making by proposing a primitive-based approach inspired by a novel cognition framework, showing that it outperforms state-of-the-art methods in classification tasks including cancer type classification.
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 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, with current state-of-the-art small and incomplete datasets 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.