Advances in affective computing have been made by combining information from different modalities, such as audio, video, and physiological signals. However, increasing the number of modalities also grows the dimensionality of the associated feature vectors, leading to higher computational cost and possibly lower prediction performance. In this regard, we present an comparative study of feature reduction methodologies for continuous emotion recognition. We compare dimensionality reduction by principal component analysis, filter-based feature selection using canonical correlation analysis, and correlation-based feature selection, as well as wrapper-based feature selection with sequential forward selection, and competitive swarm optimisation. These approaches are evaluated on the AV+EC-2015 database using support vector regression. Our results demonstrate that the wrapper-based approaches typically outperform the other methodologies, while pruning a large number of irrelevant features.