Label-Critic Tsetlin Machine: A Novel Self-supervised Learning Scheme for Interpretable Clustering

Unlike typical machine learning algorithms such as Artificial Neural Networks, the Tsetlin Machine (TM) is based on propositional logic instead of arithmetic operations, promoting it as a novel machine learning paradigm with interpretable learning outcomes. To this end, this paper proposes a self-supervised learning scheme inspired by the self-correction and interpretability provided by a standard TM. The proposed architecture uses a twin of Label-Critic Tsetlin Automata (TAs). The Label-TA learns the individual samples’ correct labels guided by a self-corrected TM logical clause. At the same time, the Critic-TA validates the learning and approves the Label-TA reward. Our empirical results on synthetic and real data show promising capabilities for self-supervised learning and interpretable clustering. Furthermore, the Label-Critic TM architecture demonstrates how propositional logic-based learning provides self-correction with the absence of the ground truths in data.