自动机与图压缩
Automata and Graph Compression
摘要：
自动机在语音处理和其他自然语言处理任务中有着广泛的应用。该框架扩展到图形压缩。类似于平稳遍历过程，我们制定了一个概率过程的图形和自动机生成，捕捉现实世界的现象，并提供了一个通用的压缩方案LZA这个概率模型。此外，我们表明，LZA显着优于其他压缩技术，如gzip和UNIX压缩命令的几个合成和真实的数据集。
英文摘要：
We present a theoretical framework for the compression of automata, which are widely used in speech processing and other natural language processing tasks. The framework extends to graph compression. Similar to stationary ergodic processes, we formulate a probabilistic process of graph and automata generation that captures real world phenomena and provide a universal compression scheme LZA for this probabilistic model. Further, we show that LZA significantly outperforms other compression techniques such as gzip and the UNIX compress command for several synthetic and real data sets.
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