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Context-Aware Clustering

机译：上下文感知群集

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摘要

Most existing methods of semi-supervised clustering introduce supervision from outside, e.g., manually label some data samples or introduce constrains into clustering results. This paper studies an interesting problem: can the supervision come from inside, i.e., the unsupervised training data themselves? If the data samples are not independent, we can capture the contextual information reflecting the dependency among the data samples, and use it as supervision to improve the clustering. This is called context-aware clustering. The investigation is substantialized on two scenarios of (1) clustering primitive visual features (e.g., SIFT features) with help of spatial contexts, and (2) clustering '0'-'9' hand written digits with help of contextual patterns among different types of features. Our context-aware clustering can be well formulated in a closed-form, where the contextual information serves as a regularization term to balance the data fidelity in original feature space and the influences of contextual patterns. A nested-EM algorithm is proposed to obtain an efficient solution, which proves to converge. By exploring the dependent structure of the data samples, this method is completely unsupervised, as no outside supervision is introduced.
机译：最现有的半监督聚类方法从外部引入监督，例如，手动标记一些数据样本或引入集群结果的约束。本文研究了一个有趣的问题：监督是否来自内部，即，无监督培训数据本身？如果数据样本不是独立的，我们可以捕获反映数据样本之间依赖性的上下文信息，并将其用作改善群集的监督。这称为上下文感知群集。在两个场景中，在空间上下文的帮助下，（1）群集的主要视觉功能（例如，SIFT功能）以及（2）群集'0' - '9'的帮助，以及不同类型的上下文模式的帮助特征。我们的上下文感知群集可以以封闭式制定良好的，其中上下文信息用作正则化术语，以平衡原始特征空间中的数据保真度以及上下文模式的影响。提出了一种嵌套 - EM算法以获得有效的解决方案，证明可以收敛。通过探索数据样本的依赖结构，这种方法完全无监视，因为介绍了外部监督。

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