Large-Scale Graph Database Indexing Based on T-mixture Model and ICA

Abstract

This paper proposes an indexing scheme based on t- mixture model and ICA, which is more robust than Gaussian mixture modeling when atypical points (or outliers) exist or the set of data has heavy tail. This indexing scheme combines optimized vector quantizer and probabilistic approximate-based indexing scheme. Experimental results on large-scale graph database show a notable efficiency improvement with optimistic precision.