Accelerating Competitive Learning Graph Quantization

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.