Dimensionality Reduction Via Proximal Manifolds

The focus of this article is on studying the descriptive proximity of manifolds in images useful in digital image pattern recognition. Extraction of low-dimensional manifolds underlying high-dimensional image data spaces leads to efficient digital image analysis controlled by fewer parameters. The end result of this approach is dimensionality reduction, important for automatic learning in pattern recognition.