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

Enhancing Haptic Distinguishability of Surface Materials with Boosting Technique