弹性核子空间聚类*

现有子空间聚类算法通常假设数据来自多个线性子空间,无法处理时间序列聚类中存在的非线性和时间轴弯曲问题.为了克服这些局限,通过引入核技巧和弹性距离,提出弹性核低秩表示子空间聚类和弹性核最小二乘回归子空间聚类,统称为弹性核子空间聚类,并从理论上证明弹性核最小二乘回归子空间算法的组效应和弹性核低秩表示子空间聚类算法的收敛性.在5个UCR时间序列数据集上的实验表明本文算法的有效性.

Elastic Kernel Subspace Clustering

In the existing subspace clustering algorithms, it is assumed that the data is derived from a union of multiple linear subspace, and these algorithms cannot deal with problems of nonlinear and time warping in time series clustering. To overcome these issues, elastic kernel low rank representation subspace clustering(EKLRR) and elastic kernel least squares regression subspace clustering(EKLSR) are proposed by introducing kernel tricks and elastic distance, and they are called elastic kernel subspace clustering(EKSC). Moreover, the grouping effect of EKLSR and the convergence of EKLRR are proved theoretically. The experimental results on five UCR datasets show the effectiveness of the proposed algorithms.