Journal of Guangdong University of Technology ›› 2019, Vol. 36 ›› Issue (05): 7-13.doi: 10.12052/gdutxb.190048

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Joint Low-Rank Representation and Graph Embedding for Unsupervised Feature Selection

Teng Shao-hua1, Feng Zhen-ye1, Teng Lu-yao2, Fang Xiao-zhao1   

  1. 1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China;
    2. Centre for Applied Informatics, Victoria University, Melbourne VIC 3011, Australia
  • Received:2019-03-25 Online:2019-08-21 Published:2019-08-06

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Abstract: Dimensionality reduction becomes a significant problem due to the proliferation of high dimensional data. For dimensionality reduction, feature selection is more analytical than feature extraction. Therefore feature selection plays an important role in pattern recognition. In recent years, many feature selection methods based on manifold learning have been proposed. However, such methods are susceptible to noise data. Therefore, an efficient unsupervised feature selection method is proposed-joint low-rank representation and graph embedding for unsupervised feature selection (JLRRGE). This method not only finds the low rank structure of the data after selecting feature, which makes the algorithm more robust, but also preserves the local manifold structure of the data through the adaptive graph embedding. The experimental results show that the proposed algorithm is superior to other compared methods.

Key words: unsupervised learning, low rank representation, graph embedding, feature selection

CLC Number: 

  • TP391.4
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