Title: Guided projections for analysing the structure of high-dimensional data,
Abstract: A powerful data transformation method named guided projections is proposed
creating new possibilities to reveal the group structure of high-dimensional
data in the presence of noise variables. Utilising projections onto a space
spanned by a selection of a small number of observations allows measuring the
similarity of other observations to the selection based on orthogonal and score
distances. Observations are iteratively exchanged from the selection creating a
non-random sequence of projections which we call guided projections. In
contrast to conventional projection pursuit methods, which typically identify a
low-dimensional projection revealing some interesting features contained in the
data, guided projections generate a series of projections that serve as a basis
not just for diagnostic plots but to directly investigate the group structure
in data. Based on simulated data we identify the strengths and limitations of
guided projections in comparison to commonly employed data transformation
methods. We further show the relevance of the transformation by applying it to
real-world data sets.