Combined generative topographic mapping and graph theory unsupervised approach for nonlinear fault identification

Matheus S. Escobar; Hiromasa Kaneko; Kimito Funatsu

doi:10.1002/aic.14748

Combined generative topographic mapping and graph theory unsupervised approach for nonlinear fault identification

AIChE Journal ◽

10.1002/aic.14748 ◽

2015 ◽

Vol 61 (5) ◽

pp. 1559-1571 ◽

Cited By ~ 13

Author(s):

Matheus S. Escobar ◽

Hiromasa Kaneko ◽

Kimito Funatsu

Keyword(s):

Graph Theory ◽

Fault Identification ◽

Topographic Mapping ◽

Generative Topographic Mapping ◽

Unsupervised Approach

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On Generative Topographic Mapping and Graph Theory combined approach for unsupervised non-linear data visualization and fault identification

Computers & Chemical Engineering ◽

10.1016/j.compchemeng.2016.12.009 ◽

2017 ◽

Vol 98 ◽

pp. 113-127 ◽

Cited By ~ 8

Author(s):

Matheus S. Escobar ◽

Hiromasa Kaneko ◽

Kimito Funatsu

Keyword(s):

Graph Theory ◽

Data Visualization ◽

Fault Identification ◽

Topographic Mapping ◽

Combined Approach ◽

Generative Topographic Mapping ◽

Non Linear

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Data Visualization & Clustering: Generative Topographic Mapping Similarity Assessment Allied to Graph Theory Clustering

Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: Jürgen Bajorath - ACS Symposium Series ◽

10.1021/bk-2016-1222.ch010 ◽

2016 ◽

pp. 175-210

Author(s):

Matheus de Souza Escobar ◽

Hiromasa Kaneko ◽

Kimito Funatsu

Keyword(s):

Graph Theory ◽

Data Visualization ◽

Topographic Mapping ◽

Generative Topographic Mapping ◽

Similarity Assessment

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Development of a patent roadmap through the Generative Topographic Mapping and Bass diffusion model

Journal of Engineering and Technology Management ◽

10.1016/j.jengtecman.2015.08.006 ◽

2015 ◽

Vol 38 ◽

pp. 53-70 ◽

Cited By ~ 18

Author(s):

Yujin Jeong ◽

Keeeun Lee ◽

Byungun Yoon ◽

Rob Phaal

Keyword(s):

Diffusion Model ◽

Topographic Mapping ◽

Generative Topographic Mapping ◽

Bass Diffusion ◽

Bass Diffusion Model

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Extraction of motor unit action potentials from electromyographic signals through generative topographic mapping

Journal of the Franklin Institute ◽

10.1016/j.jfranklin.2006.10.006 ◽

2007 ◽

Vol 344 (3-4) ◽

pp. 154-179 ◽

Cited By ~ 7

Author(s):

Adriano O. Andrade ◽

Slawomir J. Nasuto ◽

Peter Kyberd

Keyword(s):

Motor Unit ◽

Action Potentials ◽

Topographic Mapping ◽

Generative Topographic Mapping ◽

Motor Unit Action Potentials ◽

Motor Unit Action ◽

Electromyographic Signals ◽

Unit Action

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Developments of the generative topographic mapping

Neurocomputing ◽

10.1016/s0925-2312(98)00043-5 ◽

1998 ◽

Vol 21 (1-3) ◽

pp. 203-224 ◽

Cited By ~ 110

Author(s):

Christopher M. Bishop ◽

Markus Svensén ◽

Christopher K.I. Williams

Keyword(s):

Topographic Mapping ◽

Generative Topographic Mapping

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Generative Topographic Mapping for Dimension Reduction in Engineering Design

Lecture Notes in Computer Science - Learning and Intelligent Optimization ◽

10.1007/978-3-642-13800-3_19 ◽

2010 ◽

pp. 204-207 ◽

Cited By ~ 1

Author(s):

Asha Viswanath ◽

Alexander I. J. Forrester ◽

Andy J. Keane

Keyword(s):

Dimension Reduction ◽

Engineering Design ◽

Topographic Mapping ◽

Generative Topographic Mapping

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Kernel Generative Topographic Mapping of Protein Sequences

Medical Applications of Intelligent Data Analysis - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-4666-1803-9.ch013 ◽

2012 ◽

pp. 195-208

Author(s):

M.I. Cardenas ◽

A. Vellido ◽

I. Olier ◽

X. Rovira ◽

J. Giraldo

Keyword(s):

Data Analysis ◽

Kernel Method ◽

Protein Sequences ◽

Topographic Mapping ◽

Complex Data ◽

Generative Topographic Mapping ◽

Protein Amino Acid ◽

Genomics And Proteomics ◽

The World ◽

Symbolic Sequences

The world of pharmacology is becoming increasingly dependent on the advances in the fields of genomics and proteomics. The –omics sciences bring about the challenge of how to deal with the large amounts of complex data they generate from an intelligence data analysis perspective. In this chapter, the authors focus on the analysis of a specific type of proteins, the G protein-couple receptors, which are the target for over 15% of current drugs. They describe a kernel method of the manifold learning family for the analysis of protein amino acid symbolic sequences. This method sheds light on the structure of protein subfamilies, while providing an intuitive visualization of such structure.

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