Data is often nonnegative by nature, consider for example pixel intensities, amplitude spectra, occurrence counts, food consumption, user scores or stock market values. Optimal processing of such data may call for processing under nonnegativity constraints. Nonnegative matrix factorization (NMF) is a linear regression technique with growing popularity in the fields of machine learning and signal/image processing. It basically consists of approximating a data matrix with nonnegative entries as the product of two other nonnegative matrices, where one matrix acts as dictionary of learnt features and the other one acts as a matrix of activation coefficients. NMF has been applied to diverse problems (such as pattern recognition, clustering, mining, source separation and collaborative filtering) in many areas (for example bioinformatics, music analysis, text processing, finance). NMF, and its extension to nonnegative tensor factorization (NTF), are young research topics that call for answers to many open problems. The general aim of TANGERINE is to bring theoretical and methodological research contributions to NMF and NTF.
Publications (updated Apr. 2013)