论文标题
通过超级安装的分布式计算意识到的量化设计
A Distributed Computationally Aware Quantizer Design via Hyper Binning
论文作者
论文摘要
我们为分布式功能压缩设计了分布式功能感知的量化方案。我们考虑$ 2 $相关的来源$ x_1 $和$ x_2 $，以及为连续功能$ f（x_1，\，x_2）$的结果寻求估计$ \ hat {f} $的目的地。我们开发了一种称为Hyper Binning的压缩方案，以通过最大程度地减少联合源分区的熵来量化$ f $。 Hyper Binning是对使用正交套件的渐近最佳SLEPIAN-WOLD编码方案的覆盖随机代码构建的自然概括。这种方法背后的关键思想是使用线性判别分析以表征不同的源特征组合。该方案捕获了来源与函数结构之间的相关性，作为降低维度的一种手段。我们研究了针对不同源分布的超级套件的性能，并确定哪些类别的源需要更多分区，以实现更好的功能近似。我们的方法将信息理论的观点带入了信号处理的传统矢量量化技术。
We design a distributed function-aware quantization scheme for distributed functional compression. We consider $2$ correlated sources $X_1$ and $X_2$ and a destination that seeks an estimate $\hat{f}$ for the outcome of a continuous function $f(X_1,\,X_2)$. We develop a compression scheme called hyper binning in order to quantize $f$ via minimizing the entropy of joint source partitioning. Hyper binning is a natural generalization of Cover's random code construction for the asymptotically optimal Slepian-Wolf encoding scheme that makes use of orthogonal binning. The key idea behind this approach is to use linear discriminant analysis in order to characterize different source feature combinations. This scheme captures the correlation between the sources and the function's structure as a means of dimensionality reduction. We investigate the performance of hyper binning for different source distributions and identify which classes of sources entail more partitioning to achieve better function approximation. Our approach brings an information theory perspective to the traditional vector quantization technique from signal processing.
