Dan Feldman, Morteza Monemizadeh, and Christian Sohler. A ptas for k-means clustering based on weak coresets. In Proceedings of the twenty-third annual symposium on Computational geometry, pages 11-18, 2007.
Dan Feldman, Micha Feigin, and Nir Sochen. Learning big (image) data via coresets for dictionaries. Journal of mathematical imaging and vision, 46:276-291, 2013.
Manuel Fernandez, David P. Woodruff, and Taisuke Yasuda. Tight kernel query complexity of kernel ridge regression and kernel  $k$ -means clustering. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 7055-7063. PMLR, 2019. URL http://proceedings.mlr.press/v97/yasuda19a.html
J-J Fuchs. On sparse representations in arbitrary redundant bases. IEEE transactions on Information theory, 50(6):1341-1344, 2004.
William B. Johnson and Joram Lindenstrauss. Extensions of Lipschitz mappings into a Hilbert space. In Conference in modern analysis and probability (New Haven, Conn., 1982), volume 26 of Contemp. Math., pages 189-206. Amer. Math. Soc., Providence, RI, 1984. doi: 10.1090/conm/026/737400. URL https://doi.org/10.1090/conm/026/737400
Daniel M. Kane, Jelani Nelson, and David P. Woodruff. On the exact space complexity of sketching and streaming small norms. In Moses Charikar, editor, Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2010, Austin, Texas, USA, January 17-19, 2010, pages 1161-1178. SIAM, 2010.
Kasper Green Larsen and Jelani Nelson. The johnson-lindenstrauss lemma is optimal for linear dimensionality reduction. In Ioannis Chatzigiannakis, Michael Mitzenmacher, Yuval Rabani, and Davide Sangiorgi, editors, 43rd International Colloquium on Automata, Languages, and Programming, ICALP 2016, July 11-15, 2016, Rome, Italy, volume 55 of LIPics, pages 82:1-82:11. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2016.
Kasper Green Larsen and Jelani Nelson. Optimality of the johnson-lindenstrauss lemma. In Chris Umans, editor, 58th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2017, Berkeley, CA, USA, October 15-17, 2017, pages 633-638. IEEE Computer Society, 2017.
Simin Liu, Tianrui Liu, Ali Vakilian, Yulin Wan, and David P Woodruff. On learned sketches for randomized numerical linear algebra. 2020.
Stuart P. Lloyd. Least squares quantization in PCM. IEEE Trans. Inf. Theory, 28(2):129-136, 1982. doi: 10.1109/TIT.1982.1056489. URL https://doi.org/10.1109/TIT.1982.1056489.
Meena Mahajan, Prajakta Nimbhorkar, and Kasturi R. Varadarajan. The planar k-means problem is np-hard. Theor. Comput. Sci., 442:13-21, 2012. doi: 10.1016/j.tcs.2010.05.034. URL https://doi.org/10.1016/j.tcs.2010.05.034
Konstantin Makarychev, Yury Makarychev, and Ilya Razenshteyn. Performance of johnson-lindenstrauss transform for k-means and k-midians clustering. In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, pages 1027-1038, 2019.
B. K. Natarajan. Sparse approximate solutions to linear systems. SIAM J. Comput., 24(2): 227-234, 1995. doi: 10.1137/S0097539792240406. URL https://doi.org/10.1137/S0097539792240406
Jelani Nelson and Huy L. Nguyen. Lower bounds for oblivious subspace embeddings. In Javier Esparza, Pierre Fraigniaud, Thore Husfeldt, and Elias Koutsoupias, editors, Automata, Languages, and Programming - 41st International Colloquium, ICALP 2014, Copenhagen, Denmark, July 8-11, 2014, Proceedings, Part I, volume 8572 of Lecture Notes in Computer Science, pages 883-894. Springer, 2014.
Bruno A Olshausen and David J Field. Sparse coding with an overcomplete basis set: A strategy employed by v1? Vision research, 37(23):3311-3325, 1997.