Basic Level Concepts as a Means to Better Interpretability of Boolean Matrix Factors and Their Application to Clustering

摘要

We present an initial study linking in cognitive psychology well known phenomenon of basic level concepts and a general Boolean matrix factorization method. The result of this fusion is a new algorithm producing factors that explain a large portion of the input data and that are easy to interpret. Moreover, the link with the cognitive psychology allowed us to design a new clustering algorithm that groups objects into clusters that are close to human perception. In addition we present experiments that provide insight to the relationship between basic level concepts and Boolean factors.