论文标题
Nyström近似与非负基质分解
Nyström Approximation with Nonnegative Matrix Factorization
论文作者
论文摘要
通过估计远程网络系统的有利距离或地标的部分距离测量值估算接近度聚类的需求，我们表明，可以有效地提出邻近度聚类问题，以解决NyStröm近似问题，该问题解决了复杂空间中的Kernel K-Means群集问题。我们基于基于里程碑的非负矩阵分解（NMF）过程实施NyStröm近似。评估结果表明，随着我们改变参数选择和网络条件的范围，该提出的方法在合成和现实世界数据集上发现了几乎最佳的聚类质量。
Motivated by the needs of estimating the proximity clustering with partial distance measurements from vantage points or landmarks for remote networked systems, we show that the proximity clustering problem can be effectively formulated as the Nyström approximation problem, which solves the kernel K-means clustering problem in the complex space. We implement the Nyström approximation based on a landmark based Nonnegative Matrix Factorization (NMF) process. Evaluation results show that the proposed method finds nearly optimal clustering quality on both synthetic and real-world data sets as we vary the range of parameter choices and network conditions.
