JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2019, Vol. 54 ›› Issue (3): 93-101.doi: 10.6040/j.issn.1671-9352.1.2018.051

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A chunk increment partial least square algorithm

ZENG Xue-qiang1,2, YE Zhen-lin1, ZUO Jia-li2, WAN Zhong-ying2, WU Shui-xiu2   

  1. 1. Information Engineering School, Nanchang University, Nanchang 330031, Jiangxi, China;
    2. School of Computer &
    Information Engineering, Jiangxi Normal University, Nanchang 330022, Jiangxi, China
  • Published:2019-03-19

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Abstract: For the data mining of large-scale data, incremental learning is an effective and efficient technique. As an improved partial least square(PLS)method based on incremental learning, incremental partial least square(IPLS)has a competitive dimension reduction performance. However, there is a drawback in this approach that training samples must be learned one by one, which consumes a lot of time on the issue of on-line learning. To overcome this problem, we propose an extension of IPLS called chunk incremental partial least square(CIPLS)in which a chunk of training samples is processed at a time. Comparative experiments on k8 cancer rescue mutants data set and Reuter-21578 text classification corpus show the proposed CIPLS algorithm is much more efficient than IPLS without sacrifice dimension reduction performance.

Key words: incremental learning, partial least square, data chunk, dimension reduction

CLC Number: 

  • TP311
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