[1]WANG Lijuan,DING Shifei.A spectral clustering algorithm based on ELM-AE feature representation[J].CAAI Transactions on Intelligent Systems,2021,16(3):560-566.[doi:10.11992/tis.202005021]
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A spectral clustering algorithm based on ELM-AE feature representation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 3 Page number: 560-566 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2021-05-05
Title:
A spectral clustering algorithm based on ELM-AE feature representation
Author(s):
WANG Lijuan1; 2;  DING Shifei1
1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;
2. School of Information Engineering, Xuzhou College of Industrial Technology, Xuzhou 221114, China
Keywords:
spectral clustering; feature representation; extreme machine learning; auto-encoder; extreme learning machine as autoencoder; machine learning; clustering analysis; data mining
CLC:
TP391
DOI:
10.11992/tis.202005021
Abstract:
In practice, redundant features and outliers (noise) in data points heavily influence the discovery of more prominent features in clustering and significantly impair clustering performance. In this study, we propose a spectral clustering (SC) based on extreme machine learning as autoencoder (ELM-AE) feature representation (SC-ELM-AE). ELM-AE learns the principal feature representation of the source data via singular value decomposition and uses the output weights to realize reconstruction from feature representation space to the original input data. The reconstructed feature representation space is fed to the SC as input. The experimental results show that the proposed algorithm is 30% more accurate in the average clustering than the conventional K-means, SC, and other existing algorithms in the verification of five UCI datasets, particularly on complex high-dimensional datasets, such as PEMS-SF and TDT2_10.
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