Publisher
Springer International Publishing
Reference21 articles.
1. Araújo, M.C.U., Saldanha, T.C.B., Galvão, R.K.H., Yoneyama, T., Chame, H.C., Visani, V.: The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometr. Intell. Lab. Syst. 57(2), 65–73 (2001)
2. Arora, S., Ge, R., Kannan, R., Moitra, A.: Computing a nonnegative matrix factorization – provably. In: Proceedings of the Forty-Fourth Annual ACM Symposium on Theory of Computing, pp. 145–162 (2012)
3. Bioucas-Dias, J.M., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., Chanussot, J.: Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 5(2), 354–379 (2012)
4. Cohen, J.E., Gillis, N.: Nonnegative low-rank sparse component analysis. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8226–8230 (2019)
5. El Ghaoui, L., Viallon, V., Rabbani, T.: Safe feature elimination in sparse supervised learning technical report no. Technical report, UC/EECS-2010-126, EECS Department, University of California at Berkeley (2010)