We show both formally and by means of experiments that self-tuning histograms suffer from three major problems - sensitivity to learning, stagnation, and dimensionality. We also describe our solution to the problem - which is histogram initialization with subspace clustering.
['Andranik Khachatryan', 'Emmanuel Müller', 'Klemens Böhm', 'Christian Stier']
Improving accuracy and robustness of self-tuning histograms by subspace clustering