Multiway clustering via tensor block models
Multiway clustering via tensor block models
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发表时间：
2019-06
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通讯作者：
Yuchen Zeng;Miaoyan Wang
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作者：
Yuchen Zeng;Miaoyan Wang
我们考虑从一个大的噪声张量识别多路块结构的问题。在基因组学、推荐系统、主题建模和传感器网络定位等应用中经常出现这样的问题。我们提出了一个张量块模型，开发了一个统一的最小二乘估计，并获得理论上的精度保证多路聚类。统计收敛的估计建立，我们表明，相关的聚类过程实现分区一致性。稀疏正则化的进一步发展，以确定重要的块与高架手段。该提案处理广泛的数据类型，包括二进制，连续和混合观测。通过对两个真实的数据集的仿真和应用，我们证明了我们的方法优于以前的方法。
We consider the problem of identifying multiway block structure from a large noisy tensor. Such problems arise frequently in applications such as genomics, recommendation system, topic modeling, and sensor network localization. We propose a tensor block model, develop a unified least-square estimation, and obtain the theoretical accuracy guarantees for multiway clustering. The statistical convergence of the estimator is established, and we show that the associated clustering procedure achieves partition consistency. A sparse regularization is further developed for identifying important blocks with elevated means. The proposal handles a broad range of data types, including binary, continuous, and hybrid observations. Through simulation and application to two real datasets, we demonstrate the outperformance of our approach over previous methods.