A collaborative convex framework for factoring a data matrix X into a non-negative product AS, with a sparse coefficient matrix S, is introduced. We restrict the columns of the dictionary matrix A to coincide with certain columns of X, thereby guaranteeing a physically meaningful dictionary and dimensionality reduction. As an example, we show applications of the proposed framework on hyperspectral endmember and abundances identification.
CITATION STYLE
Möller, M., Esser, E., Osher, S., Sapiro, G., & Xin, J. (2010). A Convex Model for Matrix Factorization and Dimensionality Reduction on Physical Space and its Application to Blind Hyperspectral Unmixing. Evaluation (pp. 1–6). Retrieved from http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA540658
Mendeley helps you to discover research relevant for your work.