Machine fusion to enhance the topology preservation of vector quantization artificial neural networks

Artificial neural networks techniques have been successfully applied in vector quantization (VQ) encoding. The objective of VQ is to statistically preserve the topological relationships existing in a data set and to project the data to a lattice of lower dimensions, for visualization, compression, storage, or transmission purposes. However, one of the major drawbacks in the application of artificial neural networks is the difficulty to properly specify the structure of the lattice that best preserves the topology of the data. To overcome this problem, in this paper we introduce merging algorithms for machine-fusion, boosting-fusion-based and hybrid-fusion ensembles of SOM, NG and GSOM networks. In these ensembles not the output signals of the base learners are combined, but their architectures are properly merged. We empirically show the quality and robustness of the topological representation of our proposed algorithm using both synthetic and real benchmarks datasets.

Research highlights
► Ensemble techniques consisting in the fusion of architectures of vector quantization techniques were developed.
► The merging process exploits the information of the codebook vectors of the base learners together with the training data.
► Fusion schemes based on bagging, boosting and hybrid algorithms are explored.
► Empirical results show that the ensemble were able to improve the quality of topological representation compared to single networks.