The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Recentemente desenvolvemos um método para extração de características de dados multivariados usando um análogo da dinâmica de Kuramoto para modelar a sincronização coletiva em uma rede de osciladores de fase acoplados. Em nosso método, que chamamos de sincronização de dados, os osciladores de fase que transportam dados multivariados em seus ritmos naturais e atualizados alcançam sincronizações parciais. Seus ritmos comuns são interpretados como vetores modelo que representam as características gerais do conjunto de dados. Neste estudo, discutimos a ligação da sincronização de dados com o algoritmo de mapa auto-organizado como um método popular para mineração de dados e mostramos através de experimentos numéricos como nosso método pode superar as desvantagens do algoritmo de mapa auto-organizado em que seleções não intencionais de dados inadequados vetores de referência levam a padrões de recursos falsos.
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Takaya MIYANO, Takako TSUTSUI, "Link of Data Synchronization to Self-Organizing Map Algorithm" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 1, pp. 263-269, January 2009, doi: 10.1587/transfun.E92.A.263.
Abstract: We have recently developed a method for feature extraction from multivariate data using an analogue of Kuramoto's dynamics for modeling collective synchronization in a network of coupled phase oscillators. In our method, which we call data synchronization, phase oscillators carrying multivariate data in their natural and updated rhythms achieve partial synchronizations. Their common rhythms are interpreted as the template vectors representing the general features of the data set. In this study, we discuss the link of data synchronization to the self-organizing map algorithm as a popular method for data mining and show through numerical experiments how our method can overcome the disadvantages of the self-organizing map algorithm in that unintentional selections of inappropriate reference vectors lead to false feature patterns.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.263/_p
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@ARTICLE{e92-a_1_263,
author={Takaya MIYANO, Takako TSUTSUI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Link of Data Synchronization to Self-Organizing Map Algorithm},
year={2009},
volume={E92-A},
number={1},
pages={263-269},
abstract={We have recently developed a method for feature extraction from multivariate data using an analogue of Kuramoto's dynamics for modeling collective synchronization in a network of coupled phase oscillators. In our method, which we call data synchronization, phase oscillators carrying multivariate data in their natural and updated rhythms achieve partial synchronizations. Their common rhythms are interpreted as the template vectors representing the general features of the data set. In this study, we discuss the link of data synchronization to the self-organizing map algorithm as a popular method for data mining and show through numerical experiments how our method can overcome the disadvantages of the self-organizing map algorithm in that unintentional selections of inappropriate reference vectors lead to false feature patterns.},
keywords={},
doi={10.1587/transfun.E92.A.263},
ISSN={1745-1337},
month={January},}
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TY - JOUR
TI - Link of Data Synchronization to Self-Organizing Map Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 263
EP - 269
AU - Takaya MIYANO
AU - Takako TSUTSUI
PY - 2009
DO - 10.1587/transfun.E92.A.263
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2009
AB - We have recently developed a method for feature extraction from multivariate data using an analogue of Kuramoto's dynamics for modeling collective synchronization in a network of coupled phase oscillators. In our method, which we call data synchronization, phase oscillators carrying multivariate data in their natural and updated rhythms achieve partial synchronizations. Their common rhythms are interpreted as the template vectors representing the general features of the data set. In this study, we discuss the link of data synchronization to the self-organizing map algorithm as a popular method for data mining and show through numerical experiments how our method can overcome the disadvantages of the self-organizing map algorithm in that unintentional selections of inappropriate reference vectors lead to false feature patterns.
ER -