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Large-Margin Metric Learning for Partitioning Problems
[article]
2013
arXiv
pre-print
In this paper, we consider unsupervised partitioning problems, such as clustering, image segmentation, video segmentation and other change-point detection problems. We focus on partitioning problems based explicitly or implicitly on the minimization of Euclidean distortions, which include mean-based change-point detection, K-means, spectral clustering and normalized cuts. Our main goal is to learn a Mahalanobis metric for these unsupervised problems, leading to feature weighting and/or
arXiv:1303.1280v1
fatcat:wxhpxl6jenhtfnue7po6mvrnau