New Statistical Algorithms for the Analysis of Mass - FU Berlin, FB MI ...

3.8. EXTRACTING FINGERPRINTS 57
Figure 3.8.18: This shows the feature selection (FS, left) and dimensionality reduction
(DR, right) process. First, in the FS stage, from a set of potential features
(pairs of masterpeaks, top image) the best x are selected (x = 8 in this example).
Thus, each spectrum is projected onto a point in R x . These points are then projected
during the DR stage into R 2
where most clustering algorithms are easily applicable.
� Enhancing generalization
� Speeding up the learning process
� Enabling the interpretation of the model
The core idea is to apply a mapping of the high-dimensional data space
into a space of fewer dimensions. Two widespread strategies are filtering (e.g.
information gain) and wrapping (e.g. genetic algorithm) approaches. In classification
applications, feature selection may be viewed as a discrimination
problem where one aims to emphasize the class separability by a judicious
choice of feature parameters. Ideally, the extracted features should reveal
some unique non-redundant characteristics that are most effective in discriminating
between classes.
The selection of features is necessary because (a) it is mostly computationally
infeasible to use all available features in the subsequent machine learning
algorithms and (b) problems emerge when limited data samples but a large
number of features are present (this relates to the so called curse of dimensionality).
It can be shown that optimal feature selection requires an exhaustive