  Boundary points pose a significant challenge for machine learning tasks,
including classification, clustering, and dimensionality reduction. Due to the
similarity of features, boundary areas can result in mixed-up classes or
clusters, leading to a crowding problem in dimensionality reduction. To address
this challenge, numerous boundary point detection methods have been developed,
but they are insufficiently to accurately and efficiently identify the boundary
points in non-convex structures and high-dimensional manifolds. In this work,
we propose a robust and efficient method for detecting boundary points using
Local Direction Dispersion (LoDD). LoDD considers that internal points are
surrounded by neighboring points in all directions, while neighboring points of
a boundary point tend to be distributed only in a certain directional range.
LoDD adopts a density-independent K-Nearest Neighbors (KNN) method to determine
neighboring points, and defines a statistic-based metric using the eigenvalues
of the covariance matrix of KNN coordinates to measure the centrality of a
query point. We demonstrated the validity of LoDD on five synthetic datasets
(2-D and 3-D) and ten real-world benchmarks, and tested its clustering
performance by equipping with two typical clustering methods, K-means and Ncut.
Our results show that LoDD achieves promising and robust detection accuracy in
a time-efficient manner.
