We propose a novel technique for finding representatives from a large, unsupervised dataset. The approach is based on the concept of self-rank, defined as the minimum number of samples needed to reconstruct all samples with an accuracy proportional to the rank-$K$ approximation. Our proposed algorithm enjoys linear complexity w.r.t. the size of original dataset and simultaneously it provides an adaptive upper bound for approximation ratio. These favorable characteristics result in filling a historical gap between practical and theoretical methods in finding representatives.