JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2013, Vol. 43 ›› Issue (1): 28-33.

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Text categorization algorithm based on non-linear manifold learning and k-NN

ZHANG Guo-dong1,2, ZHANG Hua-xiang1,2*   

  1. 1. School of Information Science & Engineering, Shandong Normal University, Jinan 250014, China;
    2. Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan 250014, China
  • Received:2012-12-05 Online:2013-02-20 Published:2012-12-05

Abstract:

In order to save the problems of dimensionality curse, noise data in text categorization, the text categorization algorithm was presented based on the non-linear dimensionality reduction algorithm and combined with kNN(knearest neighbor algorithm). The algorithm first removed the noise data, and then used the locally linear embedding algorithm of non-linear manifold learning to recover low-dimensional manifold structure in high-dimensional data to implement dimensionality reduction. The processed data was used to construct k-NN classifiers. Experimental results showed that this  algorithm could  effectively improve the accuracy of text classification.

Key words: data reduction, categorization, non-linear dimensionality reduction algorithm, k-NN

CLC Number: 

  • TP391
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