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.

这篇论文介绍了稀疏 Tsetlin 机器（STM），一种高效处理稀疏数据的新型 Tsetlin 机器（TM）。通过引入活跃文字（AL），STM 可以集中关注当前数据表示中实际有贡献的文字，显著减少内存占用和计算时间，同时展现了有竞争力的分类性能。

稀疏特斯林机器：活动文字的稀疏表示

The Sparse Tsetlin Machine: Sparse Representation with Active Literals

In this paper, we introduce a sparse Tsetlin Machine (TM) with absorbing
Tsetlin Automata (TA) states. In brief, the TA of each clause literal has both
an absorbing Exclude- and an absorbing Include state, making the learning
scheme absorbing instead of ergodic. When a TA reaches an absorbing state, it
will never leave that state again. If the absorbing state is an Exclude state,
both the automaton and the literal can be removed from further consideration.
The literal will as a result never participates in that clause. If the
absorbing state is an Include state, on the other hand, the literal is stored
as a permanent part of the clause while the TA is discarded. A novel sparse
data structure supports these updates by means of three action lists: Absorbed
Include, Include, and Exclude. By updating these lists, the TM gets smaller and
smaller as the literals and their TA withdraw. In this manner, the computation
accelerates during learning, leading to faster learning and less energy
consumption.

通过引入具有吸收状态的稀疏 Tsetlin Machine (TM) 以及吸收态的 Tsetlin Automata (TA)，本文提出了一种新的学习方案，通过稀疏数据结构的更新，在学习过程中加速计算、提高学习速度并减少能源消耗。