Fast sequential implementation of "neural-gas" network for vector quantization

Abstract

Although the "neural-gas" network proposed by Martinetz et al. in 1993 has been proven for its optimality in vector quantizer design and has been demonstrated to have good performance in time-series prediction, its high computational complexity (N logN) makes it a slow sequential algorithm. In this letter, we suggest two ideas to speedup its sequential realization: 1) using a truncated exponential function as its neighborhood function and 2) applying a new extension of the partial distance elimination method (PDE). This fast realization is compared with the original version of the neural-gas network for codebook design in image vector quantization. The comparison indicates that a speedup of five times is possible, while the quality of the resulting codebook is almost the same as that of the straightforward realization.

Combining classifiers based on confidence values

“…Ancona et al [1] discussed the questions of sorting accuracy and sorting completeness. Choy et al [2] applied the partial distance elimination (PDE) method to speed-up the NG algorithm in the above context. In our investigations, we eliminate the explicit ordering by employing the following implicit ordering metric:…”

“…In addition, we also updated only those units with a nonnegligible effective learning rate as in [2,6]. This is implemented by a further modification to the weight updating phase with the following truncated exponential function as in [2].…”

“…This is implemented by a further modification to the weight updating phase with the following truncated exponential function as in [2].…”

“…The NG network algorithm suffers from a high time complexity problem in its sequential implementation [2,6]. In the original neural gas network, an explicit ordering of all distances between synaptic weights and the training sample was necessary.…”