Kernel Clustering with Sigmoid-based Regularization for Efficient Segmentation of Sequential Data. (arXiv:2106.11541v2 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2106.11541

Kernel segmentation aims at partitioning a data sequence into several
non-overlapping segments that may have nonlinear and complex structures. In
general, it is formulated as a discrete optimization problem with combinatorial
constraints. A popular algorithm for optimally solving this problem is dynamic
programming (DP), which has quadratic computation and memory requirements.
Given that sequences in practice are too long, this algorithm is not a
practical approach. Although many heuristic algorithms have been proposed to
approximate the optimal segmentation, they have …

arxiv clustering data kernel lg regularization segmentation