[1] Choi, S.:Algorithms for orthogonal nonnegative matrix factorization. IEEE International Joint Conference on Neural Networks. pp. 1828-1832. IEEE Press, Hong Kong (2008)
[2] Ding, C., Li, T., Peng, W., Park, H.:Orthogonal nonnegative matrix t-factorizations for clustering. In:Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 126-135. ACM Press, Philadelphia (2006)
[3] Li, X., Cui, G., Dong, Y.:Discriminative and orthogonal subspace constraints-based nonnegative matrix factorization. ACM Trans. Intell. Syst. Technol. 9(6), Article No. 65(2019)
[4] Mirzal, A.:Orthogonal nonnegative matrix factorization for blind image separation. In:International Visual Informatics Conference, pp. 25-35. Springer, Cham (2013)
[5] Tolic, D., Antulov-Fantulin, N., Kopriva, I.:A nonlinear orthogonal non-negative matrix factorization approach to subspace clustering. Pattern Recogn. 82, 40-55(2018)
[6] Wen, Z., Yin, W.:A feasible method for optimization with orthogonality constraints. Math. Program. 142(1), 397-434(2013)
[7] Li, B., Zhou, G., Cichocki, A.:Two efficient algorithms for approximately orthogonal nonnegative matrix factorization. IEEE Signal Process. Lett. 22(7), 843-846(2015)
[8] Li, P., Bu, J., Yang, Y., Ji, R., Chen, C., Cai, D.:Discriminative orthogonal nonnegative matrix factorization with flexibility for data representation. Expert Syst. Appl. 41(4), 1283-1293(2017)
[9] Li, Z., Wu, X., Peng, H.:Nonnegative matrix factorization on orthogonal subspace. Pattern Recogn. Lett. 31(9), 905-911(2010)
[10] Mirzal, A.:A convergent algorithm for orthogonal nonnegative matrix factorization. J. Comput. Appl. Math. 260(2), 149-166(2014)
[11] Ye, J., Jin, Z.:Nonnegative matrix factorization on orthogonal subspace with smoothed L0 norm constrained. In:Yang, J., Fang, F., Sun, C. (eds.) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol. 7751. Springer, Berlin (2013)
[12] Yoo, J., Choi, S.:Orthogonal nonnegative matrix factorization:multiplicative updates on Stiefel manifolds. In:Yang, J., Fang, F., Sun, C. (eds.) Intelligent Data Engineering and Automated Learning-IDEAL 2008, vol. 5326, pp. 140-147. Springer, Daejeon (2008)
[13] Pompili, F., Gillis, N., Absil, P.A., Glineur, F.:Two algorithms for orthogonal nonnegative matrix factorization with application to clustering. Neurocomputing 141, 15-25(2014)
[14] Jin, Q.G., Liang, G.L.:Fast hierarchical alternating nonnegative least squares algorithm for nonnegative matrix factorization. Comput. Simul. 29(11), 174-185(2012)
[15] Kimura, K., Kudo, M., Tanaka, Y.:A column-wise update algorithm for nonnegative matrix factorization in Bregman divergence with an orthogonal constraint. Mach. Learn. 103(2), 285-306(2017)
[16] Kimura, K., Tanaka, Y., Kudo, M.:A fast hierarchical alternating least squares algorithm for orthogonal nonnegative matrix factorization. In:Phung, D., Li, H. (eds.) Proceedings of the Sixth Asian Conference on Machine Learning, vol. 39, pp. 129-141(2014)
[17] Drineas, P., Mahoney, M.W.:Randnla:randomized numerical linear algebra. Commun. ACM 59(6), 80-90(2016)
[18] Erichson, N.B., Mendible, A., Wihlborn, S., Kutz, J.N.:Randomized nonnegative matrix factorization. Pattern Recogn. Lett. 104, 1-7(2018)
[19] Erichson, N.B., Voronin, S., Brunton, S.L., Kutz, J.N.:Randomized matrix decompositions using R. J. Stat, Softw 89(11), 1-48(2019). https://doi.org/10.18637/jss.v089.i11
[20] Halko, N., Martinsson, P.G., Tropp, J.A.:Finding structure with randomness:probabilistic algorithms for constructing approximate matrix decompositions. SIAM Rev. 53(2), 217-288(2009)
[21] Ghashami, M., Liberty, E., Phillips, J.M., Woodruff, D.P.:Frequent directions:simple and deterministic matrix sketching. SIAM J. Comput. 45(5), 1762-1792(2016)
[22] Tepper, M., Sapiro, G.:Compressed nonnegative matrix factorization is fast and accurate. IEEE Trans. Signal Process. 64(9), 2269-2283(2016)
[23] Lin, C.J.:Projected gradient methods for nonnegative matrix factorization. Neural Comput. 19(10), 2756-2779(2007)
[24] Lin, L., Liu, Z.Y.:An alternating projected gradient algorithm for nonnegative matrix factorization. Appl. Math. Comput. 217(24), 9997-10002(2011)
[25] Dai, Y.H., Han, D.R., Yuan, X.M., Zhang, W.X.:A sequential updating scheme of the Lagrange multiplier for separable convex programming. Math. Comput. 86, 315-343(2017)
[26] Deng, W., Yin, W.:On the global and linear convergence of the generalized alternating direction method of multipliers. J. Sci. Comput. 66(3), 889-916(2016)
[27] Sun, D., Toh, K.C., Yang, L.:A convergent 3-block semi-proximal alternating direction method of multipliers for conic programming with 4-type of constraints. SIAM J. Optim. 25, 882-915(2015)
[28] Wu, Z., Li, M., Wang, D.Z.W., Han, D.:A symmetric alternating direction Method of multipliers for separable nonconvex minimization problems. Asia Pac. J. Oper. Res. 34(6), 1-27(2017)
[29] Yang, J., Zhang, Y.:Alternating direction algorithms for 1-problems in compressive sensing. SIAM J. Sci. Comput. 33(1), 250-278(2009)
[30] Wang, X., Xie, X., Lu, L.:An effective initialization for orthogonal nonnegative matrix factorization. J. Comput. Math. 30(1), 34-46(2012)
[31] Nie, F., Xu, D., Li, X.:Initialization independent clustering with actively self-training method. IEEE Trans. Syst. Man Cybern. B Cybern. 42(1), 17-27(2012)