论文标题
用于光谱群集的非参数bootstrap
A Non-Parametric Bootstrap for Spectral Clustering
论文作者
论文摘要
有限混合物建模是聚类领域中一种流行的方法，并且在很大程度上是由于其软聚类成员资格概率所致。拟合有限混合模型的一种常见方法是采用光谱聚类，该聚类可以利用预期最大化（EM）算法。但是，EM算法是许多问题的受害者，包括融合了次级最佳解决方案。我们通过开发两种新型算法来解决这个问题，这些算法结合了数据矩阵的光谱分解和非参数bootstrap采样方案。模拟显示了我们算法的有效性，不仅证明了它们的灵活性，而且还展示了与其他聚类算法相比，它们的计算效率和避免溶液差的能力不足，以估算有限混合模型。与其他符合有限混合模型的自举算法相比，我们的技术在收敛性上更加一致。
Finite mixture modelling is a popular method in the field of clustering and is beneficial largely due to its soft cluster membership probabilities. A common method for fitting finite mixture models is to employ spectral clustering, which can utilize the expectation-maximization (EM) algorithm. However, the EM algorithm falls victim to a number of issues, including convergence to sub-optimal solutions. We address this issue by developing two novel algorithms that incorporate the spectral decomposition of the data matrix and a non-parametric bootstrap sampling scheme. Simulations display the validity of our algorithms and demonstrate not only their flexibility, but also their computational efficiency and ability to avoid poor solutions when compared to other clustering algorithms for estimating finite mixture models. Our techniques are more consistent in their convergence when compared to other bootstrapped algorithms that fit finite mixture models.
