Several classes of endmember (EM) extraction algorithms based on the pure pixel assumption exist. Most of these algorithms employ some geometrical interpretation of the spectral mixing process, and use orthogonal projections, random projections, or some combination of them. Random projection based algorithms, such as pixel purity index, often find clusters of EM candidates which show high correlation, requiring a manual post-processing. Pure orthogonal projection based methods such as the simplex growing algorithm always yield a single, identical set of EMs, as the iteration process is fully deterministic. Mixed methods, such as VCA, can be highly random, and produce different sets of EMs each run. In this work, we present a new EM extraction algorithm which combines the positive aspects of orthogonal projection and random projection-based methods, resulting in a method which does not require manual intervention, possesses much less randomness than VCA, and is more flexible than fixed iterative methods. These properties are illustrated on a real data set, and compared with several different types of popular EM extraction algorithms.