Unsupervised Cognition
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913178311458816 |
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| author | Ibias, Alfredo Antona, Hector Ramirez-Miranda, Guillem Guinovart, Enric Alarcon, Eduard |
| author_facet | Ibias, Alfredo Antona, Hector Ramirez-Miranda, Guillem Guinovart, Enric Alarcon, Eduard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18624 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unsupervised Cognition Ibias, Alfredo Antona, Hector Ramirez-Miranda, Guillem Guinovart, Enric Alarcon, Eduard Artificial Intelligence Machine Learning 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. |
| title | Unsupervised Cognition |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.18624 |