R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique R450 - Institut NUMEDIART pour les Technologies des Arts Numériques
European Projects :
HE - 101085607 - eLinoR - Beyond Low-Rank Factorizations
H. Zou, T. Hastie, and R. Tibshirani, “Sparse principal component analysis,” Journal of Computational and Graphical Statistics, vol. 15, no. 2, pp. 265-286, 2006.
D. D. Lee and H. S. Seung, “Learning the parts of objects by nonnegative matrix factorization,” Nature, vol. 401, no. 6755, pp. 788-791, 1999.
P. Miettinen, T. Mielikäinen, A. Gionis, G. Das, and H. Mannila, “The discrete basis problem,” IEEE Trans. Knowl. Data Eng., vol. 20, no. 10, pp. 1348-1362, 2008.
Z. Zhang, T. Li, C. Ding, and X. Zhang, “Binary matrix factorization with applications,” in IEEE Int. Conf. on Data Mining, 2007, pp. 391-400.
P. Miettinen and S. Neumann, “Recent developments in Boolean matrix factorization,” in International Joint Conference on Artificial Intelligence, 2021.
M. Udell, C. Horn, R. Zadeh, S. Boyd et al., “Generalized low rank models,” Foundations and Trends® in Machine Learning, vol. 9, no. 1, pp. 1-118, 2016.
N. Gillis, Nonnegative Matrix Factorization. Philadelphia, PA: SIAM, 2020.
C. Ding, T. Li, W. Peng, and H. Park, “Orthogonal nonnegative matrix t-factorizations for clustering,” in 12th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2006, pp. 126-135.
G. Chen, F. Wang, and C. Zhang, “Collaborative filtering using orthogonal nonnegative matrix tri-factorization,” Information Processing & Management, vol. 45, no. 3, pp. 368-379, 2009. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0306457308001167
J. Yoo and S. Choi, “Orthogonal nonnegative matrix tri-factorization for co-clustering: Multiplicative updates on stiefel manifolds,” Information Processing & Management, vol. 46, no. 5, pp. 559-570, 2010. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S0306457310000038
M. Ortiz-Bouza and S. Aviyente, “Community detection in multiplex networks based on orthogonal nonnegative matrix tri-factorization,” IEEE Access, 2024.
A. Dache, A. Vandaele, and N. Gillis, “Orthogonal symmetric nonnegative matrix tri-factorization,” in 34th IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2024.
P. Miettinen, “The Boolean column and column-row matrix decompositions,” in European Conference on Machine Learning and Knowledge Discovery in Databases, ser. ECMLPKDD’08, 2008, p. 17.
L. Liang, K. Zhu, and S. Lu, “BEM: mining coregulation patterns in transcriptomics via Boolean matrix factorization,” Bioinformatics 36 (13), pp. 4030-4037, 2020.
A. Haddad, F. Shamsi, L. Zhu, and L. Najafizadeh, “Identifying dynamics of brain function via Boolean matrix factorization,” in Asilomar Conference on Signals, Systems, and Computers, 2018.
C. Wan, W. Chang, T. Zhao, M. Li, S. Cao, and C. Zhang, “Fast and efficient Boolean matrix factorization by geometric segmentation,” in AAAI Conference on Artificial Intelligence, 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:211076357
R. Cabral Farias and S. Miron, “A generalized approach for Boolean matrix factorization,” Signal Processing, vol. 206, p. 108887, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S0165168422004261
B. Thirunavukarasu, N. Richi, and C. Yuen, “People to people recommendation using coupled nonnegative Boolean matrix factorization,” in Int. Conf. on Soft-computing and Network Security (ICSNS), 2018.
C. Kolomvakis, A. Vandaele, and N. Gillis, “Algorithms for Boolean matrix factorization using integer programming,” in 33rd International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2023.
N. Gillis, “Sparse and unique nonnegative matrix factorization through data preprocessing,” The Journal of Machine Learning Research, vol. 13, no. 1, pp. 3349-3386, 2012.
L. Grasedyck, M. Klever, and S. Krämer, “Quasi-orthogonalization for alternating non-negative tensor factorization,” Electronic Transactions on Numerical Analysis, vol. 62, pp. 22-57, 2024.
D. Dua and C. Graff, “UCI machine learning repository,” 2017. [Online]. Available: http://archive.ics.uci.edu/ml
C. Wan, P. Dang, T. Zhao, Y. Zang, C. Zhang, and S. Cao, “Bias aware probabilistic Boolean matrix factorization,” in Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, 2022, pp. 2035-2044.
S. Miron, M. Diop, A. Larue, E. Robin, and D. Brie, “Boolean decomposition of binary matrices using a post-nonlinear mixture approach,” Signal Processing, vol. 178, p. 107809, 2021.
D. Desantis, E. Skau, D. P. Truong, and B. Alexandrov, “Factorization of binary matrices: Rank relations, uniqueness and model selection of Boolean decomposition,” ACM Trans. Knowl. Discov. Data, vol. 16, no. 6, 2022. [Online]. Available: https://doi.org/10.1145/3522594