Zhou Fang
ABSTRACT
In this paper, a new framework for unsupervised partitioning problems are proposed in order to stabilize the performance of the Large-Margin Metric Learning method developed by Lajugie [lajugie et al. 2014] when the fraction of the given labels is low. The Large-Margin method could be used to deal with a wide range of partitioning problems such as clustering, image segmentation, video segmentation and change-detection problems but does not show robustness when applied to large set of data with a small number of labels. Hence, we combine the algorithm with the relevant component analysis algorithm [Bar-Hillel et al.2006] when the fraction of the labels is low and adhere to the original Large-Margin Method when the fraction is high. We also provide experiments to show that by implementing the new frame work, we can achieve more stable partitioning performance in synthetic examples.
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