多视图双聚类 via 非负矩阵三因子分解
Multi-view biclustering via non-negative matrix tri-factorisation
AI总结：
本文提出ResNMTF方法，通过非负矩阵三因子分解实现多视图双聚类，能够识别重叠和非穷尽双聚类，并引入双轮廓分数用于超参数调优和可视化。
摘要：
随着数据生产、收集和存储方法在实际和财政上的可行性不断提高，多视图数据变得愈加明显。然而，并非所有特征都适用于描述所有个体的模式。多视图双聚类旨在同时对行和列进行聚类，发现行的聚类以及其视图特定的识别特征。本文提出了一种基于非负矩阵分解的新型多视图双聚类方法，称为ResNMTF。通过在合成和真实数据集上的广泛实验，ResNMTF成功地识别了重叠和非穷尽的双聚类，无需事先知道双聚类的数量，并能够结合任何视图间的共享维度。此外，为了解决缺乏合适的双聚类内在度量的问题，流行的轮廓分数被扩展为双轮廓分数。双轮廓分数被证明与已知的外在度量一致，并且作为超参数调优和可视化工具证明是有用的。
英文摘要：
Multi-view data is ever more apparent as methods for production, collection and storage of data become more feasible both practically and fiscally. However, not all features are relevant to describe the patterns for all individuals. Multi-view biclustering aims to simultaneously cluster both rows and columns, discovering clusters of rows as well as their view-specific identifying features. A novel multi-view biclustering approach based on non-negative matrix factorisation is proposed named ResNMTF. Demonstrated through extensive experiments on both synthetic and real datasets, ResNMTF successfully identifies both overlapping and non-exhaustive biclusters, without pre-existing knowledge of the number of biclusters present, and is able to incorporate any combination of shared dimensions across views. Further, to address the lack of a suitable bicluster-specific intrinsic measure, the popular silhouette score is extended to the bisilhouette score. The bisilhouette score is demonstrated to align well with known extrinsic measures, and proves useful as a tool for hyperparameter tuning as well as visualisation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
