Prediction methods for seismic multiples are never ideal in practice and an adaptive subtraction process is needed to account for mismatches between the predicted and the actual multiples. We are interested in the problem of separating primary and multiple seismic signals based on their statistical properties. We link recent advances in the blind-source separation problem to the multiple removal problem, and present a novel adaptive subtraction method based on an information maximization principle. Compared with previous methods, our proposed method uses higher-order statistics of the data and incorporates the filtering nature of the adaptive subtraction problem into our algorithm formulation. We use simulations to show that our proposed adaptive subtraction method outperforms the popular least-squares adaptive subtraction and the independent component analysis methods quantitatively, as measured by the mean-squared error, and qualitatively, as evaluated by the visual quality of the image reconstruction.