Accelerating Competitive Learning Graph Quantization

TL;DR

This paper introduces an accelerated competitive learning graph quantization method that leverages local graph lifting to vectors, significantly speeding up the process while maintaining solution quality.

Contribution

It presents a novel acceleration technique for graph quantization that avoids costly graph distance calculations by local lifting, bridging graph and vector quantization.

Findings

Significant speedup in graph quantization process

Maintains comparable solution quality to traditional methods

Gradually transitions from graph to vector quantization with iterations

Abstract

Vector quantization(VQ) is a lossy data compression technique from signal processing for which simple competitive learning is one standard method to quantize patterns from the input space. Extending competitive learning VQ to the domain of graphs results in competitive learning for quantizing input graphs. In this contribution, we propose an accelerated version of competitive learning graph quantization (GQ) without trading computational time against solution quality. For this, we lift graphs locally to vectors in order to avoid unnecessary calculations of intractable graph distances. In doing so, the accelerated version of competitive learning GQ gradually turns locally into a competitive learning VQ with increasing number of iterations. Empirical results show a significant speedup by maintaining a comparable solution quality.