One of the most challenging issues in unsupervised target analysis is how to obtain unknown target knowledge directly
from the data to be processed. This issue has never arisen in supervised target analysis where the target knowledge is
either assumed to be known or provided by a priori. However, with recent advent of sensor technology many unknown
and subtle signal sources can be uncovered and revealed by high spectral imaging spectrometers such as hyperspectral
imaging sensors. The knowledge of these signal sources generally cannot be obtained by assumed or prior knowledge.
Under this circumstance supervised target analysis may not be realistic or applicable. This paper addresses the issue of
how to generate such knowledge for data analysis and further develops unsupervised target finding algorithms for target
analysis. In order to demonstrate the utility of the developed unsupervised target finding algorithms, experiments are
conducted for applications in unsupervised linear spectral unmixing.