Endmember extraction by exemplar finder

![CDATA[We propose a novel method called exemplar finder (EF) for spectral data endmember extraction problem, which is also known as blind unmixing in remote sensing community. Exemplar finder is based on data self reconstruction assuming that the bases (endmembers) generating the data exist in the given data set. The bases selection is fulfilled by minimising a l2/l1 norm on the reconstruction coefficients, which eliminates or suppresses irrelevant weights from non-exemplar samples. As a result, it is able to identify endmembers automatically. This algorithm can be further extended, for example, using different error structures and including rank operator. We test this method on semi-simulated hyperspectral data where ground truth is available. Exemplar finder successfully identifies endmembers, which is far better than some existing methods, especially when signal to noise ratio is high.]]