谱聚类的非参数自助法
A Non-Parametric Bootstrap for Spectral Clustering
AI总结：
针对有限混合模型中EM算法易收敛到次优解的问题，提出两种结合数据矩阵谱分解和非参数自助法抽样的新算法，模拟证明其具有更高的灵活性、计算效率和收敛一致性。
摘要：
有限混合建模是聚类领域中一种流行的方法，其优势主要在于其软聚类成员概率。拟合有限混合模型的一种常见方法是采用谱聚类，该方法可以利用期望最大化（EM）算法。然而，EM算法会遇到许多问题，包括收敛到次优解。我们通过开发两种结合了数据矩阵谱分解和非参数自助法抽样方案的新颖算法来解决这个问题。模拟展示了我们算法的有效性，并证明了与其他用于估计有限混合模型的聚类算法相比，它们不仅具有灵活性，而且在计算效率和避免劣质解的能力上表现优异。与其他拟合有限混合模型的自助法算法相比，我们的技术在收敛性上更加一致。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
