Progressive Spectral Dimensionality Process

Abstract

In order to mitigate the dependence on virtual dimensionality (VD), this chapter develops a new dimensionality reduction by transform (DRT), to be called progressive spectral dimensionality process (PSDP), which introduces a new concept of dimensionality prioritization (DP) that revolutionizes how the commonly used Dimensionality reduction (DR) is implemented. The motivation of DP arises from a need to process vast amount of hyperspectral data in a more effective manner in many applications. The DP developed in this chapter attempts to resolve the following three issues. The first and foremost is to develop a credible DR transform that can compress the original data into a spectral-transformed data space in some sense of optimality. A second issue is to represent the original data in a spectral dimensionality reduced lower data space via a DRT. Finally, a third issue is to prioritize each spectral dimension in the new reduced spectral data space.

Related Papers

• → Research on dimension reduction method for hyperspectral remote sensing image based on global mixture coordination factor analysis(2015)14 cited
• → Principal Components Analysis Competitive Learning(2004)16 cited
• → Principal Components Analysis Competitive Learning(2003)4 cited
• → Estimating Dimensionality of Hyperspectral Data Using False Neighbour Method(2008)4 cited
• → Progressive Spectral Dimensionality Process(2013)