论文标题
排名划分：形式化，说明性示例和新的集群增强策略
Rank-one partitioning: formalization, illustrative examples, and a new cluster enhancing strategy
论文作者
论文摘要
在本文中，我们介绍并形式化了一个排名的分区学习范式，该范式统一了分区方法，这些方法是通过使用单个向量进行汇总的数据集来进行的，该数据集进一步用于得出最终的聚类分区。将此统一作为起点，我们提出了一种基于等级的矩阵分解和分段恒定信号的降解的新型算法解决方案。最后，我们提出了对我们发现的经验证明，并证明了拟议的DeNoising步骤的鲁棒性。我们认为，我们的工作为几种无监督的学习技术提供了一种新的观点，有助于对数据分配的一般机制有更深入的了解。
In this paper, we introduce and formalize a rank-one partitioning learning paradigm that unifies partitioning methods that proceed by summarizing a data set using a single vector that is further used to derive the final clustering partition. Using this unification as a starting point, we propose a novel algorithmic solution for the partitioning problem based on rank-one matrix factorization and denoising of piecewise constant signals. Finally, we propose an empirical demonstration of our findings and demonstrate the robustness of the proposed denoising step. We believe that our work provides a new point of view for several unsupervised learning techniques that helps to gain a deeper understanding about the general mechanisms of data partitioning.
