A classifier-based decoding approach for large scale distributed coding
Canonical distributed quantization schemes do not scale to large sensor networks due to the exponential decoder storage complexity that they entail. Prior efforts to tackle this issue have largely been limited to the suboptimal schemes of source grouping and decoding, thus failing to use all available information at the decoder. We propose a new decoding paradigm where all received bits are used in decoding. Essentially, to decode each source, we partition the space of received bit-tuples using a nearest neighbor quantizer at a decoding rate consistent with the allowed complexity and each partition is then mapped to a reconstruction value for that source. To avoid local minima in design, we resort to deterministic annealing to determine the nearest neighbor partition function (the partitioning prototypes) from the training set. Results on several data-sets show substantial gains over naive and other competing approaches.