On Statistical Learning of Simplices: Unmixing Problem Revisited

Abstract

We study the sample complexity of learning a high-dimensional simplex from a set of points uniformly sampled from its interior. Learning of simplices is a long studied problem in computer science and has applications in computational biology and remote sensing, mostly under the name of 'spectral unmixing'. We theoretically show that a sufficient sample complexity for reliable learning of a K-dimensional simplex is O K 2 log K , which yields a significant improvement over the existing bounds. Based on our new theoretical framework, we also propose a heuristic approach for the inference of simplices. Experimental results on synthetic and realworld datasets demonstrate a comparable performance for our method on noiseless samples, while we outperform the state-of-the-art in noisy cases.