Subspace clustering is an important unsupervised clustering approach. It is based on the
assumption that the high-dimensional data points are approximately distributed around …

Automatic identification systems (AISs) serve as a complement to radar systems, and they
have been installed and widely used onboard ships to identify targets and improve …

Multiview subspace clustering has received significant attention as the availability of diverse
of multidomain and multiview real-world data has rapidly increased in the recent years …

Previous spectral clustering methods sequentially conduct three steps, ie, similarity matrix
learning from original data, spectral representation learning, and K-means clustering on …

Various problems in manifold estimation make use of a quantity called the reach, denoted by
M, which is a measure of the regularity of the manifold. This paper is the first investigation …

Nonasymptotic rates for manifold, tangent space and curvature estimation Page 1 The Annals
of Statistics 2019, Vol. 47, No. 1, 177–204 https://doi.org/10.1214/18-AOS1685 © Institute of …

Machine learning deals with datasets characterized by high dimensionality. However, in
many cases, the intrinsic dimensionality of the datasets is surprisingly low. For example, the …

We develop a method to automatically segment a font's glyphs into a set of overlapping and
intersecting strokes with the aim of generating artistic stylizations. The segmentation method …

We consider the problem of optimality in manifold reconstruction. A random sample X
_n={X_1, ..., X_n\} ⊂ R^ DX n= X 1,…, X n⊂ RD composed of points close to ad …

Image Segmentation Summarizes and improves new theory, methods, and applications of
current image segmentation approaches, written by leaders in the field The process of …