We consider the problem of clustering with the longest-leg path distance (LLPD) metric,
which is informative for elongated and irregularly shaped clusters. We prove finite-sample …

This paper proposes and analyzes a novel clustering algorithm, called learning by
unsupervised nonlinear diffusion (LUND), that combines graph-based diffusion geometry …

An unsupervised learning algorithm to cluster hyperspectral image (HSI) data that leverages
spatially regularized random walks is proposed. Markov diffusions are defined on the space …

This paper proposes and analyzes a novel clustering algorithm that combines graph-based
diffusion geometry with techniques based on density and mode estimation. The proposed …

An active learning method for the classification of high-dimensional images is proposed in
which spatially-regularized nonlinear diffusion geometry is used to characterize cluster …

We consider the problem of clustering with the longest-leg path distance (LLPD) metric,
which is informative for elongated and irregularly shaped clusters. We prove finite-sample …

We propose an active learning algorithm for labeling hyperspectral images (HSI). Pixels with
ambiguous class affinity are iteratively estimated using geometric and statistical properties of …

Multivariate time-series have become abundant in recent years, as many data-acquisition
systems record information through multiple sensors simultaneously. In this paper, we …

Multivariate time-series have become abundant in recent years, as many data-acquisition
systems record information through multiple sensors simultaneously. In this paper, we …

An algorithm for clustering hyperspectral images (HSI) based on diffusion geometry in the
space of high-dimensional image patches is proposed. By using the patch structure of the …