The problem of unsupervised learning and segmentation of hyperspectral images is a
significant challenge in remote sensing. The high dimensionality of hyperspectral data …

This paper discusses the results of a field experiment conducted at Savannah River National
Laboratory to test the performance of several algorithms for the localization of radioactive …

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

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 …

Data-dependent metrics are powerful tools for learning the underlying structure of high-
dimensional data. This article further develops and analyzes a data-dependent metric …

We propose a novel unsupervised learning algorithm that makes use of image fusion to
efficiently cluster remote sensing data. Exploiting nonlinear structures in multimodal data, 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 …

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

Data-dependent metrics are powerful tools for learning the underlying structure of high-
dimensional data. This article develops and analyzes a data-dependent metric known as …

The problem is to evaluate the behavior of an object when primary sources of information
about the object become unavailable, so that any information must be obtained from the …