论文标题
走向弱源编码
Towards Weak Source Coding
论文作者
论文摘要
在本文中，作者提供了Claude Shannon的传统源编码定理的弱解码版本。 The central bound that is obtained is \[ χ>\log_ε(2^{-n(H(X)+ε)}) \] where \[ χ=\frac{\log(k)}{n(H(X)+ε)} \] and $k$ is the number of unsupervised learning classes formed out of the non-typical source sequences.边界得出的结论是，如果类的数量足够高，则可能会提高可靠性函数。这种改进可能允许的特定方案是非典型序列簇尺寸较小且放置稀疏的一种。类似的政权也可能会显示出改进。
In this paper, the authors provide a weak decoding version of the traditional source coding theorem of Claude Shannon. The central bound that is obtained is \[ χ>\log_ε(2^{-n(H(X)+ε)}) \] where \[ χ=\frac{\log(k)}{n(H(X)+ε)} \] and $k$ is the number of unsupervised learning classes formed out of the non-typical source sequences. The bound leads to the conclusion that if the number of classes is high enough, the reliability function might possibly be improved. The specific regime in which this improvement might be allowable is the one in which the atypical-sequence clusters are small in size and sparsely placed; similar regimes might also show an improvement.
