Learning to cluster using high order graphical models with latent variables

N. Komodakis
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引用次数: 13

Abstract

This paper proposes a very general max-margin learning framework for distance-based clustering. To this end, it formulates clustering as a high order energy minimization problem with latent variables, and applies a dual decomposition approach for training this model. The resulting framework allows learning a very broad class of distance functions, permits an automatic determination of the number of clusters during testing, and is also very efficient. As an additional contribution, we show how our method can be generalized to handle the training of a very broad class of important models in computer vision: arbitrary high-order latent CRFs. Experimental results verify its effectiveness.
学习使用具有潜在变量的高阶图形模型聚类
本文提出了一个非常通用的基于距离聚类的最大边际学习框架。为此,本文将聚类问题表述为具有潜在变量的高阶能量最小化问题,并采用对偶分解方法对该模型进行训练。生成的框架允许学习非常广泛的距离函数类,允许在测试期间自动确定簇的数量,并且非常高效。作为额外的贡献,我们展示了如何将我们的方法推广到处理计算机视觉中非常广泛的一类重要模型的训练:任意高阶潜在crf。实验结果验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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