Dimensionality Reduction Via Proximal Manifolds
نویسندگان
چکیده
The focus of this article is on studying the descriptive proximity of manifolds in images useful in digital image pattern recognition. Extraction of lowdimensional 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.
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