Variable selection in multi-block regression

被引：55

作者：

Biancolillo, Alessandra ^{[1
,2
]}

Liland, Kristian Hovde ^{[1
,3
]}

Mage, Ingrid ^{[1
]}

Naes, Tormod ^{[1
,2
]}

Bro, Rasmus ^{[2
]}

机构：

[1] Nofima AS, Osloveien 1,POB 210, N-1431 As, Norway

[2] Univ Copenhagen, Fac Life Sci, Dept Food Sci, Qual & Technol, Rolighedsvej 30, DK-1958 Frederiksberg C, Denmark

[3] Norwegian Univ Life Sci, Dept Chem Biotechnol & Food Sci, POB 5003, N-1432 As, Norway

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2016年 / 156卷

关键词：

Variable selection; Multi-block; SO-PLS; MB-PLS; Raman; Sensory; PARTIAL LEAST-SQUARES; NEAR-INFRARED SPECTROSCOPY; MODELS; PLS;

D O I：

10.1016/j.chemolab.2016.05.016

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The focus of the present paper is to propose and discuss different procedures for performing variable selection in a multi-block regression context. In particular, the focus is on two multi-block regression methods: Multi-Block Partial Least Squares (MB-PLS) and Sequential and Orthogonalized Partial Least Squares (SO-PLS) regression. A small simulation study for regular PLS regression was conducted in order to select the most promising methods to investigate further in the multi-block context. The combinations of three variable selection methods with MB-PLS and SO-PLS are examined in detail. These methods are Variable Importance in Projection (VIP) Selectivity Ratio (SR) and forward selection. In this paper we focus on both prediction ability and interpretation. The different approaches are tested on three types of data: one sensory data set, one spectroscopic (Raman) data set and a number of simulated multi-block data sets. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：89 / 101

页数：13