吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (06): 1626-1630.doi: 10.7964/jdxbgxb201306031

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Dimension reduction for data streams based on Johnson-Lindenstrauss transform

YANG Jing, ZHAO Jia-shi, ZHANG Jian-pei   

  1. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
  • Received:2012-07-20 Online:2013-11-01 Published:2013-11-01

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Abstract:

A dimension reduction method is proposed for data streams based on Johnson-Lindenstrauss transform to meet the requirement of real time performance, which can also be used in asynchronous cases. This method uses a Johnson-Lindenstrauss transform to project the stream data from high dimensional space to low dimensional space, and the pairwise distance is preserved after the projection. The transform matrix is constructed by random hash functions, and the random hash functions are generated by the seeds, therefore the dimension reduction process is completed at the same time of the updates. This method reduces the computational complexity, and an algorithmic sublinear-time dimension reduction of data stream is gained. The experimental results show that this method can improve the efficiency and get a good accuracy.

Key words: artificial intelligence, Johnson-Lindenstrauss transform, asynchronization data stream

CLC Number: 

  • TP18

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