ccnn.py: Zhang, Y., Liang, P., & Wainwright, M. J. (2016). Convexified convolutional neural networks. arXiv preprint arXiv:1609.01000.cur.py: Mahoney, M. W., & Drineas, P. (2009). CUR matrix decompositions for improved data analysis. Proceedings of the National Academy of Sciences, 106(3), 697-702.fastRG.py: Rohe, K., Tao, J., Han, X., & Binkiewicz, N. (2017). A note on quickly sampling a sparse matrix with low rank expectation. arXiv preprint arXiv:1703.02998.linear_time_svd: Drineas, P., Kannan, R., & Mahoney, M. W. (2006). Fast Monte Carlo algorithms for matrices II: Computing a low-rank approximation to a matrix. SIAM Journal on computing, 36(1), 158-183.matrix_completion.py: Lin, Z., Chen, M., & Ma, Y. (2010). The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv preprint arXiv:1009.5055.neighborhood_smoothing.py: Zhang, Y., Levina, E. and Zhu, J. (2016) Estimating neighborhood edge probabilities by neighborhood smoothing. arXiv preprint arXiv: 1509.08588.parafac2.py: Kiers, H. A., Ten Berge, J. M., & Bro, R. (1999). PARAFAC2-Part I. A direct fitting algorithm for the PARAFAC2 model. Journal of Chemometrics, 13(3-4), 275-294.robust_pca.py: Wright, J., Ganesh, A., Rao, S., Peng, Y., & Ma, Y. (2009). Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization. Advances in neural information processing systems, 2080-2088.randomized_svd.java: Halko, N., Martinsson, P. G., & Tropp, J. A. (2011). Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions. SIAM review, 53(2), 217-288.regularized_matrix_regression.py: Zhou, H., & Li, L. (2014). Regularized matrix regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(2), 463-483.
yunjhongwu/matrix-routines
Implementations of matrix factorization/completion (and something related) algorithms
Python