Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (3): 124-129.doi: 10.11896/jsjkx.200700078

• Database & Big Data & Data Science • Previous Articles     Next Articles

Smooth Representation-based Semi-supervised Classification

WANG Xing1 , KANG Zhao2   

  1. 1 School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China
    2 School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China
  • Received:2020-07-11 Revised:2020-10-01 Online:2021-03-15 Published:2021-03-05
  • About author:WANG Xing,born in 2000,undergra-duate.Her main research interests include machine learning,data mining and deep learning.
    KANG Zhao,born in 1983,Ph.D,asso-ciate professor,is a member of China Computer Federation.His main research interests include machine lear-ning and pattern recognition.
  • Supported by:
    National Natural Science Foundation of China(61806045).

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Abstract: Graph-based semi-supervised classification is a hot topic in machine learning and data mining.In general,this method discovers the hidden information by constructing a graph and predicts the labels for unlabeled samples based on the structural information of graph.Thus,the performance of semi-supervised classification heavily depends on the quality of graph.In this work,we propose to perform semi-supervised classification in a smooth representation.In particular,a low-pass filter is applied on the data to achieve a smooth representation,which in turn is used for semi-supervised classification.Furthermore,a unified framework which integrates graph construction and label propagation is proposed,so that they can be mutually improved and avoid the sub-optimal solution caused by low-quality graph.Extensive experiments on face and subject data sets show that the proposed SRSSC outperforms other state-of-the-art methods in most cases,which validates the significance of smooth representation.

Key words: Graph-based method, Representation learning, Semi-supervised classification, Signal filtering, Similarity measure

CLC Number: 

  • TP181
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