Dimensionality reduction is the mapping of data from a high dimensional space to a lower dimension space such that the result obtained by analyzing the reduced dataset is a good approximation to the result obtained by analyzing the original data set. There are several dimensionality reduction approaches which include Random Projections, Principal Component Analysis, the Variance approach, LSA-T...

Hyperspectral data potentially contain more information than multispectral data because of their higher spectral resolution. However, the stochastic data analysis approaches that have been successfully applied to multispectral data are not as effective for hyperspectral data as well. Various investigations indicate that the key problem that causes poor performance in the stochastic approaches t...

hyperspectral data potentially contain more information than multispectral data because of their higher spectral resolution. however, the stochastic data analysis approaches that have been successfully applied to multispectral data are not as effective for hyperspectral data as well. various investigations indicate that the key problem that causes poor performance in the stochastic approaches t...

High dimensional spaces pose a serious challenge to the learning process. It is a combination of limited number of samples and high dimensions that positions many problems under the “curse of dimensionality”, which restricts severely the practical application of density estimation. Many techniques have been proposed in the past to discover embedded, locally-linear manifolds of lower dimensional...

Visualizing how the parts of a document relate to each other and producing automatically generated quality measures that people can understand are means that writers can use to improve the quality of their compositions. This paper presents a novel document visualization technique and a measure of quality based on the average semantic distance between parts of a document. We show how the visuali...