The objective and automated monitoring of depression using behavioral signals is confounded by the wide clinical profile of this commonly occurring mood disorder. This paper introduces Relevance Vector Machines, a novel method for predicting clinical depression scores from paralinguistic cues. It highlights many of the advantages RVM can offer depression prediction; sparsity, implicit noise characterization, an explicit probabilistic output and heterogeneous mapping property which allow one or more arbitrary, non-linear, transform to be used in conjunction with a RVM. Results indicate that RVMs can perform as strongly as Support Vector Regression in a brute-forcing paradigm. Of particular interest is the heterogeneous mapping property which improves RVM performance without requiring an expensive, in terms of data and time, search of the operating parameter space.
Bibliographic reference. Cummins, Nicholas / Sethu, Vidhyasaharan / Epps, Julien / Krajewski, Jarek (2015): "Relevance vector machine for depression prediction", In INTERSPEECH-2015, 110-114.