Nyström Approximation with Nonnegative Matrix Factorization
This addresses proximity clustering for remote networked systems, but appears incremental as it combines existing Nyström and NMF methods.
The paper tackled the proximity clustering problem with partial distance measurements by formulating it as a Nyström approximation problem and implementing it using landmark-based Nonnegative Matrix Factorization, achieving nearly optimal clustering quality on synthetic and real-world datasets.
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.