A Novel Approach Based on Relevance Learning Vector Quantization Applied to the Inference of High-Order SNPs Interactions

2017 ◽  
Author(s):  
Flavia Roberta Barbosa Araujo ◽  
Katia Silva Guimaraes
Keyword(s):  
Vector Quantization ◽  
Learning Vector Quantization ◽  
High Order ◽  
Novel Approach ◽  
Relevance Learning

scholarly journals Cyber-Physical Security with RF Fingerprint Classification through Distance Measure Extensions of Generalized Relevance Learning Vector Quantization

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Trevor J. Bihl ◽  
Todd J. Paciencia ◽  
Kenneth W. Bauer ◽  
Michael A. Temple
Keyword(s):  
Vector Quantization ◽  
Distance Measure ◽  
Critical Infrastructure ◽  
Low Cost ◽  
Learning Vector Quantization ◽  
Distance Measures ◽  
Rf Signals ◽  
Communication Devices ◽  
Rf Fingerprinting ◽  
Relevance Learning

Radio frequency (RF) fingerprinting extracts fingerprint features from RF signals to protect against masquerade attacks by enabling reliable authentication of communication devices at the “serial number” level. Facilitating the reliable authentication of communication devices are machine learning (ML) algorithms which find meaningful statistical differences between measured data. The Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is one ML algorithm which has shown efficacy for RF fingerprinting device discrimination. GRLVQI extends the Learning Vector Quantization (LVQ) family of “winner take all” classifiers that develop prototype vectors (PVs) which represent data. In LVQ algorithms, distances are computed between exemplars and PVs, and PVs are iteratively moved to accurately represent the data. GRLVQI extends LVQ with a sigmoidal cost function, relevance learning, and PV update logic improvements. However, both LVQ and GRLVQI are limited due to a reliance on squared Euclidean distance measures and a seemingly complex algorithm structure if changes are made to the underlying distance measure. Herein, the authors (1) develop GRLVQI-D (distance), an extension of GRLVQI to consider alternative distance measures and (2) present the Cosine GRLVQI classifier using this framework. To evaluate this framework, the authors consider experimentally collected Z-wave RF signals and develop RF fingerprints to identify devices. Z-wave devices are low-cost, low-power communication technologies seen increasingly in critical infrastructure. Both classification and verification, claimed identity, and performance comparisons are made with the new Cosine GRLVQI algorithm. The results show more robust performance when using the Cosine GRLVQI algorithm when compared with four algorithms in the literature. Additionally, the methodology used to create Cosine GRLVQI is generalizable to alternative measures.

scholarly journals Functional relevance learning in generalized learning vector quantization

2012 ◽  
Vol 90 ◽  
pp. 85-95 ◽  
Author(s):  
Marika Kästner ◽  
Barbara Hammer ◽  
Michael Biehl ◽  
Thomas Villmann
Keyword(s):  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Functional Relevance ◽  
Relevance Learning

Generalized relevance learning vector quantization

2002 ◽  
Vol 15 (8-9) ◽  
pp. 1059-1068 ◽  
Author(s):  
Barbara Hammer ◽  
Thomas Villmann
Keyword(s):  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Relevance Learning
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