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An Unsupervised feature extraction method based on self coded neural network

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conference contribution
posted on 2019-01-01, 00:00 authored by L Hao, F Jiang
© Published under licence by IOP Publishing Ltd. The way to tap the inherent law of ECG data and feature extraction is of vital in the absence of prior knowledge of the situation. This paper presents a self coded neural network model, which is reconstructed after compression of ECG data. The compression process eliminates the redundancy of data within the original data firstly from the low dimensional with more concise representations. In order to verify the features extracted from the neural network, we select the simplest and most direct distance based K nearest neighbour classification method. By using a self compiled neural network to extract features from the data set, the classification accuracy of K nearest neighbour classification can be greatly improved. The experimental results show that the unsupervised feature extraction method based on self coded neural network can effectively extract the features of the data in practical applications.

History

Event

Advanced Algorithms and Control Engineering. Conference (2019 : Guilin, China)

Volume

1213

Issue

4

Series

Journal of Physics: Conference Series

Pagination

1 - 6

Publisher

IOP Publishing

Location

Guilin, China

Place of publication

Bristol, Eng.

Start date

2019-04-26

End date

2019-04-28

ISSN

1742-6588

eISSN

1742-6596

Language

eng

Publication classification

E1.1 Full written paper - refereed

Title of proceedings

ICAACE 2019 : Proceedings of the International Conference on Advanced Algorithms and Control Engineering (ICAACE 2019) 26–28 April 2019, Guilin, China

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