Neural incremental attribute learning based on principal component analysis

Ting Wang; Fangzhou Liu; Wei Zhou; Xiaoyan Zhu; Sheng Uei Guan

doi:10.1109/ICBDA.2016.7509830

Neural incremental attribute learning based on principal component analysis

Ting Wang, Fangzhou Liu, Wei Zhou, Xiaoyan Zhu, Sheng Uei Guan

Department of Computing

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

1 Citation (Scopus)

Abstract

Feature Extraction (FE) based on Principal Component Analysis (PCA) can effectively improve classification results by reducing the interference among features. However, such a good method has not been employed in previous studies of Incremental Attribute Learning (IAL), a novel machine learning strategy, where features are gradually trained one by one in order to remove interference among features and improve classification results. This study proposed a preprocessing for neural IAL algorithm based on feature extraction with PCA. Experimental results show that this approach is not only very efficient, but also applicable for pattern classification performance improvement.

Original language	English
Title of host publication	Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467395908
DOIs	https://doi.org/10.1109/ICBDA.2016.7509830
Publication status	Published - 12 Jul 2016
Event	2016 IEEE International Conference on Big Data Analysis, ICBDA 2016 - Hangzhou, China Duration: 12 Mar 2016 → 14 Mar 2016

Publication series

Name	Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016

Conference

Conference	2016 IEEE International Conference on Big Data Analysis, ICBDA 2016
Country/Territory	China
City	Hangzhou
Period	12/03/16 → 14/03/16

Keywords

Feature Extraction
Incremental Attribute Learning
Neural Networks
Principal Component Analysis
pattern Classification

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10.1109/ICBDA.2016.7509830

Cite this

Wang, T., Liu, F., Zhou, W., Zhu, X., & Guan, S. U. (2016). Neural incremental attribute learning based on principal component analysis. In Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016 Article 7509830 (Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBDA.2016.7509830

@inproceedings{e8e0a228d49545138310ae9db97f7786,

title = "Neural incremental attribute learning based on principal component analysis",

abstract = "Feature Extraction (FE) based on Principal Component Analysis (PCA) can effectively improve classification results by reducing the interference among features. However, such a good method has not been employed in previous studies of Incremental Attribute Learning (IAL), a novel machine learning strategy, where features are gradually trained one by one in order to remove interference among features and improve classification results. This study proposed a preprocessing for neural IAL algorithm based on feature extraction with PCA. Experimental results show that this approach is not only very efficient, but also applicable for pattern classification performance improvement.",

keywords = "Feature Extraction, Incremental Attribute Learning, Neural Networks, Principal Component Analysis, pattern Classification",

author = "Ting Wang and Fangzhou Liu and Wei Zhou and Xiaoyan Zhu and Guan, {Sheng Uei}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016 ; Conference date: 12-03-2016 Through 14-03-2016",

year = "2016",

month = jul,

day = "12",

doi = "10.1109/ICBDA.2016.7509830",

language = "English",

series = "Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016",

}

Wang, T, Liu, F, Zhou, W, Zhu, X & Guan, SU 2016, Neural incremental attribute learning based on principal component analysis. in Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016., 7509830, Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016, Hangzhou, China, 12/03/16. https://doi.org/10.1109/ICBDA.2016.7509830

Neural incremental attribute learning based on principal component analysis. / Wang, Ting; Liu, Fangzhou; Zhou, Wei et al.
Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7509830 (Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Neural incremental attribute learning based on principal component analysis

AU - Wang, Ting

AU - Liu, Fangzhou

AU - Zhou, Wei

AU - Zhu, Xiaoyan

AU - Guan, Sheng Uei

PY - 2016/7/12

Y1 - 2016/7/12

N2 - Feature Extraction (FE) based on Principal Component Analysis (PCA) can effectively improve classification results by reducing the interference among features. However, such a good method has not been employed in previous studies of Incremental Attribute Learning (IAL), a novel machine learning strategy, where features are gradually trained one by one in order to remove interference among features and improve classification results. This study proposed a preprocessing for neural IAL algorithm based on feature extraction with PCA. Experimental results show that this approach is not only very efficient, but also applicable for pattern classification performance improvement.

AB - Feature Extraction (FE) based on Principal Component Analysis (PCA) can effectively improve classification results by reducing the interference among features. However, such a good method has not been employed in previous studies of Incremental Attribute Learning (IAL), a novel machine learning strategy, where features are gradually trained one by one in order to remove interference among features and improve classification results. This study proposed a preprocessing for neural IAL algorithm based on feature extraction with PCA. Experimental results show that this approach is not only very efficient, but also applicable for pattern classification performance improvement.

KW - Feature Extraction

KW - Incremental Attribute Learning

KW - Neural Networks

KW - Principal Component Analysis

KW - pattern Classification

UR - http://www.scopus.com/inward/record.url?scp=84981298724&partnerID=8YFLogxK

U2 - 10.1109/ICBDA.2016.7509830

DO - 10.1109/ICBDA.2016.7509830

M3 - Conference Proceeding

AN - SCOPUS:84981298724

T3 - Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016

BT - Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016

Y2 - 12 March 2016 through 14 March 2016

ER -

Wang T, Liu F, Zhou W, Zhu X, Guan SU. Neural incremental attribute learning based on principal component analysis. In Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 7509830. (Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016). doi: 10.1109/ICBDA.2016.7509830

Neural incremental attribute learning based on principal component analysis

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