Fused Adjacency Matrices to enhance information extraction: the beer benchmark

Multivariate exploratory data analysis allows revealing patterns and extracting information from
complex multivariate data sets. However, highly complex data may not show evident groupings or
trends in the principal component space, e.g. because the variation of the variables are not grouped
but rather continuous. In these cases, classical exploratory methods may not provide satisfactory
results when the aim is to find distinct groupings in the data.
To enhance information extraction in such situations, we propose a novel approach inspired by the
concept of combining weak classifiers, but in the unsupervised context. The approach is based on
the fusion of several adjacency matrices obtained by different distance measures on data from
different analytical platforms. This paper is intended to present and discuss the potential of the
approach through a benchmark data set of beer samples. The beer data were acquired using three
spectroscopic techniques: Visible, near-Infrared and Nuclear Magnetic Resonance.
The results of fusing the three data sets via the proposed approach are compared with those from the
single data blocks (Visible, NIR and NMR) and from a standard mid-level data fusion methodology.
It is shown that, with the suggested approach, groupings related to beer style and other features are
efficiently recovered, and generally more evident.