Sparse Projection Pursuit Analysis: An Alternative for Exploring Multivariate Chemical Data

Sparse projection pursuit analysis (SPPA), a new approach
for the
unsupervised exploration of high-dimensional chemical data, is proposed
as an alternative to traditional exploratory methods such as principal
components analysis (PCA) and hierarchical cluster analysis (HCA).
Where traditional methods use variance and distance metrics for data
compression and visualization, the proposed method incorporates the
fourth statistical moment (kurtosis) to access interesting subspaces
that can clarify relationships within complex data sets. The quasi-power
algorithm used for projection pursuit is coupled with a genetic algorithm
for variable selection to efficiently generate sparse projection vectors
that improve the chemical interpretability of the results while at
the same time mitigating the problem of overmodeling. Several multivariate
chemical data sets are employed to demonstrate that SPPA can reveal
meaningful clusters in the data where other unsupervised methods cannot.