Variable selection in multi-block regression

By A. Biancolillo, Kristian Hovde Liland, I. Måge, Tormod Næs, Rasmus Bro. 2016

62 citations recorded by the source corpus.

Abstract 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.

Subfields: Analytical Chemistry, Biomedical Engineering, Food Science

View Paper: https://www.semanticscholar.org/paper/d7c76c86b5fe22ceb1e3bc83cedddfae4bdb0dee

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In Analytical Chemistry

1. Fang-Xiang Wu
2. Alioune Ngom
3. Yifeng Li

Citations in the corpus

• A review on machine learning principles for multi-view biological data integration (2016, background): “The attraction of PLS-based integrative models is their capability of selecting co-varying features among multiple feature sets [120].”