提出一种基于认知框架的无监督决策方法，更接近人类思维模式。
Unsupervised Cognition
- 用分布式分层结构建模输入空间，不依赖具体数据特征。
- 在癌症分类等任务上超越现有最先进无监督方法。
- 展现类认知行为，适合研究智能本质与小样本学习者。
无监督学习方法对认知模型有潜在启发意义。目前最成功的无监督方法多围绕数学空间中的样本聚类展开。本文提出一种受新型认知框架启发的、基于基础单元的无监督决策方法。该表示中心的方法以输入无关的方式，将输入空间构造成一个分布式分层结构。我们在多个场景下对比了该方法：包括当前最先进的无监督分类、小而不完备数据集的分类，以及癌症类型分类。结果表明，所提方法优于现有最先进水平。此外，我们评估了其类认知特性，发现它不仅在性能上超越对比算法（甚至包括监督学习方法），还表现出更具认知特征的行为。
原文摘要 · Abstract (English)
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.
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