基于加权集成Nyström采样的谱聚类算法

针对Nyström方法在谱聚类应用中存在聚类效果不稳定、样本代表性较弱的问题,提出基于加权集成Nyström采样的谱聚类算法.首先利用统计杠杆分数区别数据间的重要程度,对数据进行加权.然后基于权重采用加权K-means中心点采样,得到多组采样点.再引入集成框架,利用集群并行运行Nyström方法构建近似核矩阵.最后利用岭回归方法组合各个近似核矩阵,产生比标准Nyström方法更准确的低秩近似.在UCI数据集上的测试实验表明,文中算法取得较理想的聚类结果.

Spectral Clustering Algorithm Based on Weighted Ensemble Nyström Sampling

Since most Nyström methods have problems of unstable clustering effect and weak representativeness in spectral clustering application,a spectral clustering algorithm based on weighted ensemble Nyström sampling is proposed. Firstly, the statistical leverage score is used to distinguish the importance of data and the data are weighted. Then, based on these weights, the weighted K-means center point sampling is used to obtain multiple sets of sampling points. The integration framework is introduced, and the approximate kernel matrix is constructed using the cluster parallel operation Nyström method. Finally, the approximate kernel is determined by the ridge regression method. The matrices are combined to produce a more accurate low rank approximation than that by standard Nyström method. Experiments on UCI datasets demonstrate that the proposed algorithm achieves better clustering results.