Mode and Ridge Estimation in Euclidean and Directional Product Spaces: A Mean Shift Approach
研究了在欧几里得和方向度量组合的乘积空间中，从点云数据估计局部众数和密度脊的方法，扩展了均值漂移算法并证明了收敛性，对处理混合类型数据的研究者有用。
The set of local modes and density ridge lines are important summary characteristics of the data-generating distribution. In this work, we focus on estimating local modes and density ridges from point cloud data in a product space combining two or more Euclidean and/or directional metric spaces. Specifically, our approach extends the (subspace constrained) mean shift algorithm to such product spaces, addressing potential challenges in the generalization process. We establish the algorithmic convergence of the proposed methods, along with practical implementation guidelines. Experiments on simulated and real-world datasets demonstrate the effectiveness of our proposed methods. Supplementary materials for this article are available online.