i-Vector feature representation with probabilistic linear discriminant analysis (PLDA) scoring in speaker recognition system has recently achieved effective performance even on channel mismatch conditions. In general, experiments carried out using this combined strategy employ linear discriminant analysis (LDA) after the i-Vector extraction phase to suppress irrelevant directions, such as those introduced by noise or channel distortions. However, speaker-related and -non-related variability present in the data may prevent LDA from finding the best projection matrix. In this study, we exclusively use support vectors of each class to find the optimum linear transformation. Post-processing of the i-Vectors by discriminant analysis via support vectors (SVDA) and traditional LDA is evaluated on NIST2010 speaker recognition evaluation (SRE) core and extended core (coreext) conditions. In addition, truncated coreext test data is used to examine the performance of the system for both long and short duration test segments. Computed equal error rate (EER) and minimum detection cost function (minDCF) criteria confirm consistent improvement of SVDA over traditional LDA. The relative improvement in terms of EER and minDCF with SVDA are about 32% and 9%, respectively.