This chapter presents strategies for selecting representative node subsets from large datasets while maintaining approximation accuracy. Among these, the Efficient Reduced Basis Algorithm (ERBA) iteratively removes nodes based on residual or power function criteria. Unlike greedy methods that add nodes progressively, ERBA starts with the full dataset and systematically reduces it. A crucial aspect is exploiting the Extended Rippa’s Algorithm to compute validation errors efficiently, Numerical experiments demonstrate that ERBA produces sparse interpolation sets achieving comparable accuracy to full datasets with significant computational savings.