Abstract Biclustering can be defined as the simultaneous clustering of rows and columns in a data matrix and it has been recently applied to many scientific scenarios such as bioinformatics, text analysis and computer vision to name a few. In this paper we propose a novel biclustering approach, that is based on the concept of dominant-set clustering and extends such algorithm to the biclustering problem. In more detail, we propose a novel encoding of the biclustering problem as a graph so to use the dominant set concept to analyse rows and columns simultaneously. Moreover, we extend the Dominant Set Biclustering approach to facilitate the insertion of prior knowledge that may be available on the domain. We evaluated the proposed approach on a synthetic benchmark and on two computer vision tasks: multiple structure recovery and region-based correspondence. The empirical evaluation shows that the method achieves promising results that are comparable to the state-of-the-art and that outperforms competitors in various cases.

中文翻译：

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具有支配集的双聚类

摘要 双聚类可以定义为数据矩阵中行和列的同时聚类，最近它已应用于许多科学场景，例如生物信息学、文本分析和计算机视觉等。在本文中，我们提出了一种新的双聚类方法，该方法基于主导集聚类的概念，并将这种算法扩展到双聚类问题。更详细地，我们提出了一种将双聚类问题作为图的新颖编码，以便使用主导集概念同时分析行和列。此外，我们扩展了主导集 Biclustering 方法，以促进插入域中可能可用的先验知识。我们在综合基准和两个计算机视觉任务上评估了所提出的方法：多重结构恢复和基于区域的对应。实证评估表明，该方法取得了可与最先进技术相媲美的有希望的结果，并且在各种情况下都优于竞争对手。