Neural incremental attribute learning based on principal component analysis

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

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

2 Citations (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 languageEnglish
Title of host publicationProceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467395908
DOIs
Publication statusPublished - 12 Jul 2016
Event2016 IEEE International Conference on Big Data Analysis, ICBDA 2016 - Hangzhou, China
Duration: 12 Mar 201614 Mar 2016

Publication series

NameProceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016

Conference

Conference2016 IEEE International Conference on Big Data Analysis, ICBDA 2016
Country/TerritoryChina
CityHangzhou
Period12/03/1614/03/16

Keywords

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

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