title: [Parallel PLS algorithm using MapReduce and its aplication in spectral modeling].
abstract: Partial least squares (PLS) has been widely used in spectral analysis and modeling, and it is computation-intensive and time-demanding when dealing with massive data To solve this problem effectively, a novel parallel PLS using MapReduce is proposed, which consists of two procedures, the parallelization of data standardizing and the parallelization of principal component computing. Using NIR spectral modeling as an example, experiments were conducted on a Hadoop cluster, which is a collection of ordinary computers. The experimental results demonstrate that the parallel PLS algorithm proposed can handle massive spectra, can significantly cut down the modeling time, and gains a basically linear speedup, and can be easily scaled up.
authors.affiliation: School of Electric Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China. yanghuihua@tsinghua.edu.cn
authors.forename: Hui-Hua
authors.initials: HH
authors.lastname: Yang
authors.forename: Ling-Ling
authors.initials: LL
authors.lastname: Du
authors.forename: Ling-Qiao
authors.initials: LQ
authors.lastname: Li
authors.forename: Tian-Biao
authors.initials: TB
authors.lastname: Tang
authors.forename: Tuo
authors.initials: T
authors.lastname: Guo
authors.forename: Qiong-Lin
authors.initials: QL
authors.lastname: Liang
authors.forename: Yi-Ming
authors.initials: YM
authors.lastname: Wang
authors.forename: Guo-An
authors.initials: GA
authors.lastname: Luo