贪婪兼并降级具有最优幂律
Greedy-Merge Degrading has Optimal Power-Law
摘要：
考虑具有给定输入分布的通道。我们的目标是将其降级为具有最多L个输出字母的通道。一种这样的降级方法是所谓的“贪婪合并”算法。我们推导出一个上界的输入和输出之间的互信息的减少。对于固定的输入字母表大小和变量L，上限是在算法独立的下限的常数因子内。因此，我们建立贪婪合并是最佳的幂律意义上。
英文摘要：
Consider a channel with a given input distribution. Our aim is to degrade it to a channel with at most L output letters. One such degradation method is the so called "greedy-merge" algorithm. We derive an upper bound on the reduction in mutual information between input and output. For fixed input alphabet size and variable L, the upper bound is within a constant factor of an algorithm-independent lower bound. Thus, we establish that greedy-merge is optimal in the power-law sense.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
