Hierarchical Quadratic Random Forest Classifier
Faezeh Fallah
In this paper, we proposed a hierarchical quadratic random forest classifier
for classifying multiresolution samples extracted from multichannel data. This
forest incorporated a penalized multivariate linear discriminant in each of its
decision nodes and processed squared features to realize quadratic decision
boundaries in the original feature space. The penalized discriminant was based
on a multiclass sparse discriminant analysis and the penalization was based on
a group Lasso regularizer which was an intermediate between the Lasso and the
ridge regularizer. The classification probabilities estimated by this forest
and the features learned by its decision nodes could be used standalone or
foster graph-based classifiers.