  When processing high-dimensional datasets, a common pre-processing step is
feature selection. Filter-based feature selection algorithms are not tailored
to a specific classification method, but rather rank the relevance of each
feature with respect to the target and the task. This work focuses on a
graph-based, filter feature selection method that is suited for multi-class
classifications tasks. We aim to drastically reduce the number of selected
features, in order to create a sketch of the original data that codes valuable
information for the classification task. The proposed graph-based algorithm is
constructed by combing the Jeffries-Matusita distance with a non-linear
dimension reduction method, diffusion maps. Feature elimination is performed
based on the distribution of the features in the low-dimensional space. Then, a
very small number of feature that have complementary separation strengths, are
selected. Moreover, the low-dimensional embedding allows to visualize the
feature space. Experimental results are provided for public datasets and
compared with known filter-based feature selection techniques.
