Discrimination of murmurs in heart sounds is accomplished by means of time-frequency representations (TFR) which help to deal with non-stationarity. Nevertheless, classification with TFR is not straightforward given their large dimension and redundancy. In this paper we compare several methodologies to apply Principal Component Analysis (PCA) to TFR as a dimensional reduction scheme, which differ in the form that features are represented. Besides, we propose a method which maximizes information among TFR preserving information within TFRs. Results show that the methodologies that represent TFRs as matrices improve discrimination of heart murmurs, and that the proposed methodology shrinks variability of the results.