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Semi-Supervised Kernel Mean Shift Clustering

机译:半监督核均值漂移聚类

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摘要

Mean shift clustering is a powerful nonparametric technique that does not require prior knowledge of the number of clusters and does not constrain the shape of the clusters. However, being completely unsupervised, its performance suffers when the original distance metric fails to capture the underlying cluster structure. Despite recent advances in semi-supervised clustering methods, there has been little effort towards incorporating supervision into mean shift. We propose a semi-supervised framework for kernel mean shift clustering (SKMS) that uses only pairwise constraints to guide the clustering procedure. The points are first mapped to a high-dimensional kernel space where the constraints are imposed by a linear transformation of the mapped points. This is achieved by modifying the initial kernel matrix by minimizing a log det divergence-based objective function. We show the advantages of SKMS by evaluating its performance on various synthetic and real datasets while comparing with state-of-the-art semi-supervised clustering algorithms.
机译:均值漂移聚类是一种强大的非参数技术,不需要先验聚类数量,也不限制聚类的形状。但是,由于完全不受监督,当原始距离度量无法捕获基础群集结构时,其性能会受到影响。尽管最近在半监督聚类方法方面取得了进展,但几乎没有任何努力将监督纳入均值漂移。我们提出了一种用于内核均值漂移聚类(SKMS)的半监督框架,该框架仅使用成对约束来指导聚类过程。首先将这些点映射到高维内核空间,在该空间中通过映射点的线性变换施加约束。这是通过最小化基于对数偏差的目标函数来修改初始内核矩阵来实现的。通过与各种最新的半监督聚类算法进行比较,我们通过评估SKMS在各种综合数据集和实际数据集上的性能来展示其优势。

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