A powerful method in the analysis of datasets where there are many natural clusters with
varying statistics such as different sizes, shapes, density distribution, overlaps, etc., is the …

In this paper we elaborate on the challenges of learning manifolds that have many relevant
clusters, and where the clusters can have widely varying statistics. We call such data …

The self-organizing map (SOM) is a powerful method for manifold learning because of
producing a 2-D spatially ordered quantization of a higher dimensional data space on a rigid …

High-dimensional, large, and noisy data with complex structure challenge the limits of many
clustering algorithms including modern graph segmentation methods. SOM-based clustering …

We describe a proof of concept for class knowledge transfer from a labeled hyperspectral
image to an unlabeled image, captured with a different (hyper-/multi-spectral) sensor, when …

The generalization ability of a machine learning algorithm varies on the specified values to
the model-hyperparameters and the degree of noise in the learning dataset. If the dataset …

As topology representing networks, the Cumulative ADJacency graph CADJ and its
symmetric version CONN= CADJ+ CADJ T Tasdemir and Merenyi (IEEE Trans Neural Netw …

Abstract The Self-Organizing Map (SOM), which projects a (high-dimensional) data manifold
onto a lower-dimensional (usually 2-d) rigid lattice, is a commonly used manifold learning …

In develo** algorithms that exploit model-generated data, it is important to understand the
realism of the data generated by that model. One way to address this issue is to exercise a …

Hyperspectral imagery provides ample signal content to identify and distinguish between
spectrally similar, but compositionally unique, materials, but representative training samples …