Mean shift-based clustering for misaligned functional data
针对函数数据中的错位问题，改进了欧氏空间中的均值漂移算法，将其应用于平方根速度函数轨道商空间，并配备弹性距离，实现了错位函数数据的聚类。通过模拟和真实数据验证了算法的有效性。
Misalignment often occurs in functional data and can severely impact their clustering results. A clustering algorithm for misaligned functional data is developed, by adapting the original mean shift algorithm in the Euclidean space . This mean shift algorithm is applied to the quotient space of the orbits of the square root velocity functions induced by the misaligned functional data, in which the elastic distance is equipped. Convergence properties of this algorithm are studied. The efficacy of the algorithm is demonstrated through simulations and various real data applications.