凸双聚类
Convex Biclustering
摘要：
在双聚类问题中，我们寻求同时对观测和特征进行分组。虽然双聚类在广泛的领域中有应用，从文本挖掘到协同过滤，但在高维基因组数据中识别结构的问题激发了这项工作。在这种情况下，双聚类使我们能够识别仅在实验条件的子集内共表达的基因子集。我们提出了一个凸制定的biclustering问题，拥有一个独特的全球最小和迭代算法，COBRA，这是保证识别it. Our方法产生一个完整的解决方案路径的可能biclusters作为一个单一的调整参数是不同的。我们还展示了如何减少选择这个调整参数的问题，以解决一个平凡的修改凸双聚类问题。我们的工作的关键贡献是它的简单性，可解释性和算法保证-功能，可以说是缺乏目前的替代算法。我们证明了我们的方法的优点，其中包括稳定和可重复地识别双聚类，模拟和真实的微阵列数据。
英文摘要：
In the biclustering problem, we seek to simultaneously group observations and features. While biclustering has applications in a wide array of domains, ranging from text mining to collaborative filtering, the problem of identifying structure in high dimensional genomic data motivates this work. In this context, biclustering enables us to identify subsets of genes that are co-expressed only within a subset of experimental conditions. We present a convex formulation of the biclustering problem that possesses a unique global minimizer and an iterative algorithm, COBRA, that is guaranteed to identify it. Our approach generates an entire solution path of possible biclusters as a single tuning parameter is varied. We also show how to reduce the problem of selecting this tuning parameter to solving a trivial modification of the convex biclustering problem. The key contributions of our work are its simplicity, interpretability, and algorithmic guarantees - features that arguably are lacking in the current alternative algorithms. We demonstrate the advantages of our approach, which includes stably and reproducibly identifying biclusterings, on simulated and real microarray data.
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。
