In this correspondence, we propose a novel class of learning vector quantizers (LVQ’s) based on multivariate data ordering principles. A special case of the novel LVQ class is the median LVQ, which uses either the marginal median or the vector median as a multivariate estimator of location. The performance of the proposed marginal median LVQ in color image quantization is demonstrated by experi...

Reinforcement learning has proven to be a set of successful techniques for finding optimal policies on uncertain and/or dynamic domains, such as the RoboCup. One of the problems on using such techniques appears with large state and action spaces, as it is the case of input information coming from the Robosoccer simulator. In this paper, we describe a new mechanism for solving the states general...

Speech coding systems for mobile communication have to cope with noisy channels. In particular, vector quantization as central data reduction scheme is highly sensitive to transmission errors due to the low redundancy in the encoded data. Here we present three methods for the design of a vector quantizer with enhanced robustness against transmission errors. First the optimization of the index a...

Machine learning algorithms have been shown to be highly effective in solving optimization problems a wide range of applications. Such typically use gradient descent with backpropagation and the chain rule. Hence, fails if intermediate gradients are zero for some functions computational graph, because it causes collapse when multiplying zero. Vector quantization is one those challenging machine...