  Discriminative features are crucial for several learning applications, such
as object detection and classification. Neural networks are extensively used
for extracting discriminative features of images and speech signals. However,
the lack of large datasets in the haptics domain often limits the applicability
of such techniques. This paper presents a general framework for the analysis of
the discriminative properties of haptic signals. We demonstrate the
effectiveness of spectral features and a boosted embedding technique in
enhancing the distinguishability of haptic signals. Experiments indicate our
framework needs less training data, generalizes well for different predictors,
and outperforms the related state-of-the-art.
