NNMF for Image Processing Derivation

NNMF (Nonnegative Matrix Factorization) can be used to approximate high-dimensional data having nonnegative components. Lee and Seung (1999) demonstrated its use as a sum-by-parts representation of image data in order to both identify and classify image features. Xu et al. (2003) demonstrated how NNMF-based indexing could outperform SVD-based Latent Semantic Indexing (LSI) for some information retrieval tasks