Sparse linearized iterative coherence estimation (SLICE) and risk assessment in image analysis

Many methods form manifold learning have been proposed recently to accurately embed some high dimensional sets of points into low dimensional space. Most of these methods make assumptions about the spectral support of the high dimensional space being sampled and the consistency of these assumptions over time. Additionally, most of these methods do not directly incorporate a means of assessing the embedding in terms of probability distributions for estimation and detection purposes. Finally, most of these methods do not take into consideration noise in the estimation of the true underlying space. We propose a new method using sparse coherence-based estimation of distributions of points sampled from a high dimensional space that iteratively refines its notion of the support of the space. This approach will enable a new method of estimation, detection, and identification risk analysis and mitigation in a general class of image analysis problems.