We present an algorithm that efficiently computes optimal partitions of sequential data into 1 to N segments and propose a method to determine the most salient segmentation among them. As a by-product, we obtain a regularization parameter that can be used to compute such salient segmentations - also on new data sets - even more efficiently.
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