2 research outputs found
A Distributed Computationally Aware Quantizer Design via Hyper Binning
We design a distributed function aware quantization scheme for distributed
functional compression. We consider correlated sources and and
a destination that seeks the outcome of a continuous function .
We develop a compression scheme called hyper binning in order to quantize
via minimizing 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 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