  This paper introduces the Sparse Tsetlin Machine (STM), a novel Tsetlin
Machine (TM) that processes sparse data efficiently. Traditionally, the TM does
not consider data characteristics such as sparsity, commonly seen in NLP
applications and other bag-of-word-based representations. Consequently, a TM
must initialize, store, and process a significant number of zero values,
resulting in excessive memory usage and computational time. Previous attempts
at creating a sparse TM have predominantly been unsuccessful, primarily due to
their inability to identify which literals are sufficient for TM training. By
introducing Active Literals (AL), the STM can focus exclusively on literals
that actively contribute to the current data representation, significantly
decreasing memory footprint and computational time while demonstrating
competitive classification performance.
