  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 up to a total-variation error of $\epsilon$ is
$O\left(\frac{K^2}{\epsilon}\log\frac{K}{\epsilon}\right)$, which yields a
substantial improvement over existing bounds. Based on our new theoretical
framework, we also propose a heuristic approach for the inference of simplices.
Experimental results on synthetic and real-world datasets demonstrate a
comparable performance for our method on noiseless samples, while we outperform
the state-of-the-art in noisy cases.
