Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

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

R. Salas, C. Saavedra, H. Allende, C. MoragaPRL, 2011

Presented by Hung-Yi Cai2011/5/25

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Outlines· Motivation· Objectives· Methodology· Experiments· Conclusions· Comments

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Motivation

· The objective of VQ is to preserve the topological relationships existing in a data set and to project the data to a lattice of lower dimensions.

· It’s difficult to properly specify the structure of the lattice that best preserves the topology of the data.

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Objectives

· To propose a merging algorithms for machine-fusion, boosting-fusion-based and hybrid-fusion ensembles of SOM, NG and GSOM networks.

BoostingBagging

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I. M.Methodology Machine fusion method for the ensemble of VQ-ANN

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I. M.Methodology Machine fusion method for the ensemble of VQ-ANN

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I. M.Methodology Boosting machine fusion method

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I. M.Experiments· Synthetic Data

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I. M.Experiments· Real Data

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Conclusions· The main goal of this paper is to improve the

topology preservation by combining the output of several VQ-ANN.

· The proposed ensemble schemes were able to improve the quality of topological representation compared to their respective base single networks.

Intelligent Database Systems Lab

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Comments

· Advantages─ Improving the VQ in the ANN.

· Drawbacks─ The methods don’t consider the outliers.

· Applications─ Vector Quantization