Learning decentralized goal-based vector-quantization

Abstract

This paper considers the following generalization of classical vector quantization: to (vector) quantize or, equivalently, to partition the domain of a given function such that each cell in the partition satisfies a given set of topological constraints. This formulation is called decentralized goalbased vector quantization (DGVQ). The formulation is motivated by the resource allocationmechanism design problem from economics. A learning algorithm is proposed for the problem. Various extensions of the problem, as well as the corresponding modifications in the proposed algorithm, are discussed. Simulation results of the proposed algorithm for DGVQ and its extensions, are given.