[1] Johnson, W., Lindenstrauss, J.:Extensions of Lipschitz mappings into a Hilbert space. Contemp. Math. 26(189-206), 1(1984) [2] Vempala, S.:The Random Projection Method, vol. 65. American Mathematical Soc (2005) [3] Kanerva, P., Kristoferson, J., Holst, A.:Random indexing of text samples for latent semantic analysis. In:Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 22(2000) [4] Bingham, E., Mannila, H.:Random projection in dimensionality reduction:applications to image and text data. In:Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 245-250(2001) [5] Manning, C., Raghavan, P., Schütze, H.:Introduction to Information Retrieval. Cambridge University Press (2008) [6] Leskovec, J., Rajaraman,A., Ullman, J.:Mining of Massive Data Sets. Cambridge University Press (2020) [7] Achlioptas, D.:Database-friendly random projections:Johnson-Lindenstrauss with binary coins. J. Comput. Syst. Sci. 66(4), 671-687(2003) [8] Dasgupta, A., Kumar, R., Sarlós, T.:A sparse Johnson-Lindenstrauss transform. In:Proceedings of the 42nd ACM Symposium on Theory of Computing, pp. 341-350(2010) [9] Kane, D., Nelson, J.:Sparser Johnson-Lindenstrauss transforms. J. ACM 61(1), 1-23(2014) [10] Dasgupta, S., Stevens, C., Navlakha, S.:A neural algorithm for a fundamental computing problem. Science 358(6364), 793-796(2017) [11] Lin, A., Bygrave, A., DeCalignon, A., Lee, T., Miesenböck, G.:Sparse, decorrelated odor coding in the mushroom body enhances learned odor discrimination. Nat. Neurosci. 17(4), 559(2014) [12] Zheng, Z., Lauritzen, S., Perlman, E., Robinson, C., et al.:A complete electron microscopy volume of the brain of adult drosophila melanogaster. Cell 174(3), 730-743(2018) [13] Allen-Zhu, Z., Gelashvili, R., Micali, S., Shavit, N.:Sparse sign-consistent Johnson-Lindenstrauss matrices:compression with neuroscience-based constraints. Proc. Natl. Acad. Sci. 111(47), 16872-16876(2014) [14] Larsen, K., Nelson, J.:Optimality of the Johnson-Lindenstrauss lemma. In:2017 IEEE 58th Annual Symposium on Foundations of Computer Science, pp. 633-638. IEEE (2017) [15] Li, P., Hastie, T., Church, K.:Very sparse random projections. In:Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 287-296(2006) [16] Bourgain, J., Dirksen, S., Nelson, J.:Toward a unified theory of sparse dimensionality reduction in Euclidean space. Geom. Funct. Anal. 25(4), 1009-1088(2015) [17] Ailon,N.,Chazelle,B.:ThefastJohnson-Lindenstrausstransformandapproximatenearestneighbors. SIAM J. Comput. 39(1), 302-322(2009) [18] Jagadeesan, M.:Understanding sparse JL for feature hashing. In:Advances in Neural Information Processing Systems, pp. 15177-15187(2019) [19] Olsen, S., Bhandawat, V., Wilson, R.:Divisive normalization in olfactory population codes. Neuron 66(2), 287-299(2010) [20] Papadopoulou, M., Cassenaer, S., Nowotny, T., Laurent, G.:Normalization for sparse encoding of odors by a wide-field interneuron. Science 332(6030), 721-725(2011) [21] Stevens, C.:What the fly's nose tells the fly's brain. Proc. Natl. Acad. Sci. 112(30), 9460-9465(2015) [22] Li, W.:Modeling winner-take-all competition in sparse binary projections. In:Machine Learning and Knowledge Discovery in Databases, pp. 456-472. Springer, Cham (2021) [23] Li, W., Mao, J., Zhang, Y., Cui, S.:Fast similarity search via optimal sparse lifting. In:Advances in Neural Information Processing Systems, pp. 176-184(2018) [24] Ma, C., Gu, C., Li, W., Cui, S.:Large-scale image retrieval with sparse binary projections. In:Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1817-1820(2020) [25] Bennett, G.:Probability inequalities for the sum of independent random variables. J. Am. Stat. Assoc. 57(297), 33-45(1962) [26] Boucheron, S., Lugosi, G., Massart, P.:Concentration Inequalities:A Nonasymptotic Theory of Independence. Oxford University Press (2013) [27] Pennington, J., Socher, R., Manning, C.:GloVe:global vectors for word representation. In:Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1532-1543(2014) [28] Russakovsky, O., Deng, J., Su, H., et al.:Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211-252(2015) [29] Lewis, D., Yang, Y., Rose, T., Li, F.:RCV1:a new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361-397(2004) [30] Lehmann, E., Romano, J.:Testing Statistical Hypotheses. Springer (2006) [31] Andoni, A., Indyk, P.:Near-optimal hashing algorithms for near neighbor problem in high dimension. Commun. ACM 51(1), 117-122(2008) [32] Rachkovskij, D.:Vector data transformation using random binary matrices. Cybern. Syst. Anal. 50(6), 960-968(2014) |