Premium
A similarity index for comparing coupled matrices
Author(s) -
Indahl Ulf G.,
Næs Tormod,
Liland Kristian Hovde
Publication year - 2018
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3049
Subject(s) - principal component analysis , similarity (geometry) , linear subspace , data mining , redundancy (engineering) , partial least squares regression , mathematics , multivariate statistics , consistency (knowledge bases) , procrustes analysis , computer science , dimensionality reduction , set (abstract data type) , data set , statistics , artificial intelligence , geometry , image (mathematics) , programming language , operating system
Application of different multivariate measurement technologies to the same set of samples is an interesting challenge in many fields of applied data analysis. Our proposal is a 2‐stage similarity index framework for comparing 2 matrices in this type of situation. The first step is to identify factors (and associated subspaces) of the matrices by methods such as principal component analysis or partial least squares regression to provide good (low‐dimensional) summaries of their information content. Thereafter, statistical significances are assigned to the similarity values obtained at various factor subset combinations by considering orthogonal projections or Procrustes rotations and how to express the results compactly in corresponding summary plots. Applications of the methodology include the investigation of redundancy in spectroscopic data and the investigation of assessor consistency or deviations in sensory science. The proposed methodology is implemented in the R‐package “MatrixCorrelation” available online from CRAN.