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Generalized learning vector quantization : application of autoencoder as preprocessing technique to GLVQ

  • In the following study we evaluated capabilities of how a simple autoencoder can be used to trainGeneralized Learning Vector Quantization classifier. Specifically, we proved that the bottlenecks of an autoencoder serve as an "information filter" which tries to best represent the desired output in that particular layer in the statistical sense of mutual information. Autoencoder model was trained for purely unsupervised task and leveraged the advantages by learning feature representations. As a result, the model got the significant value of the accuracy. Implementation and tuning of the model was carried out using Tensor Flow [1]. An extra study has been dedicated to improve traditional GLVQ algorithm taken from sklearn-lvg [2] using the bottleneck from an autoencoder. The study has revealed potential of bottlenecks of an autoencoder as pre-processing tool in improving the accuracy of GLVQ. Specifically, the model was capable to identify 75% improvements of accuracy in GLVQ comparing to original one, which has about 62%. Consequently, the research exposed the need for further improvement of the model in the present problem case.

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Author:Anora Alieva
Advisor:Thomas Villmann, Tina Geweniger
Document Type:Master's Thesis
Year of Completion:2018
Granting Institution:Hochschule Mittweida
Release Date:2019/10/15
GND Keyword:Zeitreihe; Vektor; Lernendes System
Institutes:Angewandte Computer‐ und Bio­wissen­schaften
Dewey Decimal Classification:006.31 Maschinelles Lernen
Open Access:Innerhalb der Hochschule
Licence (German):License LogoEs gilt das UrhG