Semi-Supervised Dictionary Learning of Sparse Representations for Emotion Recognition

This work presents a technique for the classification of emotions in human-computer interaction. Based on biophysiological data, a dictionary learning approach is used to generate sparse representations of blood volume pulse signals. Such features are then used for classification of the current emotion. Unlabeled data, i.e. data without information about class membership, is used to enrich the dictionary learning stage. Superior representation abilities of the underlying structure of the data are demonstrated by the learnt dictionaries. As a result, classification rates are improved. Experimental validation in the form of different classification experiments is presented. The results are presented with a discussion about the benefits of the approach and the existing limitations.

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