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Smoothed Separable Nonnegative Matrix Factorization. (arXiv:2110.05528v2 [eess.SP] UPDATED)
stat.ML updates on arXiv.org arxiv.org
Given a set of data points belonging to the convex hull of a set of vertices,
a key problem in linear algebra, signal processing, data analysis and machine
learning is to estimate these vertices in the presence of noise. Many
algorithms have been developed under the assumption that there is at least one
nearby data point to each vertex; two of the most widely used ones are vertex
component analysis (VCA) and the successive projection algorithm (SPA). This
assumption is …
algebra algorithm algorithms analysis arxiv data data analysis factorization least linear linear algebra machine machine learning matrix noise pixel processing projection set signal spa