Model selection for partial least squares based dimension reduction

Authors:
Guo-Zheng Li;Rui-Wei Zhao;Hai-Ni Qu;Mingyu You
Affiliations:
The MOE Key Laboratory of Embedded System and Service Computing, Department of Control Science and Engineering, Tongji University, Shanghai 201804, China;The MOE Key Laboratory of Embedded System and Service Computing, Department of Control Science and Engineering, Tongji University, Shanghai 201804, China;The MOE Key Laboratory of Embedded System and Service Computing, Department of Control Science and Engineering, Tongji University, Shanghai 201804, China;The MOE Key Laboratory of Embedded System and Service Computing, Department of Control Science and Engineering, Tongji University, Shanghai 201804, China
Venue:
Pattern Recognition Letters
Year:
2012

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Abstract

Partial least squares (PLS) has been widely applied to process scientific data sets as an effective dimension reduction technique. The main way to determine the number of dimensions extracted by PLS is by using the cross validation method, but its computation load is heavy. Researchers presented fixing the number at three, but intuitively it's not suitable for all data sets. Based on the intrinsic connection between PLS and the structure of data sets, two novel algorithms are proposed to determine the number of extracted principal components, keeping the valuable information while excluding the trivial. With the merits of variety with different data sets and easy implementation, both algorithms exhibit better performance than the previous works on nine real world data sets.