Local Density Estimation in High Dimensions

Published Online:https://doi.org/10.1287/moor.2021.1221

References

  • [1] Ahle TD, Aumüller M, Pagh R (2017) Parameter-free locality sensitive hashing for spherical range reporting. Proc. 28th Annual ACM-SIAM Sympos. Discrete Algorithms (Society for Industrial and Applied Mathematics), 239–256.Google Scholar
  • [2] Andoni A, Razenshteyn I (2015) Optimal data-dependent hashing for approximate near neighbors. Proc. 47th Annual ACM Sympos. Theory Comput. (ACM, New York), 793–801.Google Scholar
  • [3] Andoni A, Razenshteyn I (2015) Tight lower bounds for data-dependent locality-sensitive hashing. Preprint, submitted July 15, https://arxiv.org/abs/1507.04299.Google Scholar
  • [4] Andoni A, Indyk P, Laarhoven T, Razenshteyn I, Schmidt L (2015) Practical and optimal LSH for angular distance. Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 28 (Neurips 2015, Montreal, Canada), 1225–1233.Google Scholar
  • [5] Arya S, da Fonseca GD, Mount DM (2010) A unified approach to approximate proximity searching. Algorithms – ESA 2010, 18th Annual European Symposium, Liverpool, UK, September 6–8, 2010. Proceedings, Part I.Google Scholar
  • [6] Aumüller M, Christiani T, Pagh R, Silvestri F (2017) Distance-sensitive hashing Preprint, submitted March 22, https://arxiv.org/abs/1703.07867.Google Scholar
  • [7] Cao S, Lu W, Xu Q (2015) GraRep: Learning graph representations with global structural information. Proc. 24th ACM Internat. Conf. Inform. Knowledge Management (ACM), 891–900.Google Scholar
  • [8] Charikar M (2002) Similarity estimation techniques from rounding algorithms. Proc. 34th Annual ACM Sympos. Theory Comput. (ACM, New York), 380–388.Google Scholar
  • [9] Charikar M, Siminelakis P (2017) Hashing-based-estimators for kernel density in high dimensions. IEEE Sympos. Foundations Comput. Sci.Google Scholar
  • [10] Coleman B, Shrivastava A, Baraniuk RG (2019) Race: Sub-linear memory sketches for approximate near-neighbor search on streaming data. Preprint, submitted February 18, https://arxiv.org/abs/1902.06687.Google Scholar
  • [11] Datar M, Immorlica N, Indyk P, Mirrokni VS (2004) Locality-sensitive hashing scheme based on p-stable distributions. Proc. 20th Annual Sympos. Comput. Geometry (ACM), 253–262.Google Scholar
  • [12] Goemans MX, Williamson DP (1995) Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. J. ACM 42(6):1115–1145.CrossrefGoogle Scholar
  • [13] Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM), 855–864.Google Scholar
  • [14] Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Proc. 31st Internat. Conf. Neural Inform. Processing Systems, 1025–1035.Google Scholar
  • [15] Huang EH, Socher R, Manning CD, Ng AY (2012) Improving word representations via global context and multiple word prototypes. Proc. 50th Annual Meeting Assoc. Comput. Linguistics: Long Papers, vol. 1 (Association for Computational Linguistics, Stroudsburg, PA), 873–882.Google Scholar
  • [16] Indyk P, Motwani R (1998) Approximate nearest neighbors: Toward removing the curse of dimensionality. Proc. 30th Annual ACM Sympos. Theory Comput. (ACM, New York), 604–613.Google Scholar
  • [17] Jegou H, Douze M, Schmid C (2011) Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Machine Intelligence 33(1):117–128.CrossrefGoogle Scholar
  • [18] Kennedy C, Ward R (2016) Fast cross-polytope locality-sensitive hashing. Preprint, submitted February 22, https://arxiv.org/abs/1602.06922.Google Scholar
  • [19] Lai S, Liu K, He S, Zhao J (2016) How to generate a good word embedding. IEEE Intelligent Systems 31(6):5–14.CrossrefGoogle Scholar
  • [20] Livni R, Shalev-Shwartz S, Shamir O (2014) On the computational efficiency of training neural networks. Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ, eds. Advances in Neural Information Processing Systems, vol. 27 (Curran Associates, Inc., Montreal, Quebec, Canada), 855–863.Google Scholar
  • [21] Luo C, Shrivastava A (2018) Arrays of (locality-sensitive) count estimators (ACE): Anomaly detection on the edge. Proc. 2018 World Wide Web Conf. (International World Wide Web Conferences Steering Committee), 1439–1448.Google Scholar
  • [22] Lv Q, Josephson W, Wang Z, Charikar M, Li K (2007) Multi-probe LSH: Efficient indexing for high-dimensional similarity search. Proc. 33rd Internat. Conf. Very Large Data Bases (VLDB Endowment), 950–961.Google Scholar
  • [23] Melamud O, McClosky D, Patwardhan S, Bansal M (2016) The role of context types and dimensionality in learning word embeddings Preprint, submitted January 5, https://arxiv.org/abs/1601.00893.Google Scholar
  • [24] Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space Preprint, submitted January 16, https://arxiv.org/abs/1301.3781.Google Scholar
  • [25] Motwani R, Naor A, Panigrahi R (2006) Lower bounds on locality sensitive hashing. Proc. 22nd Annual Sympos. Comput. Geometry (ACM), 154–157.Google Scholar
  • [26] O’Donnell R, Wu Y, Zhou Y (2014) Optimal lower bounds for locality-sensitive hashing (except when q is tiny). ACM Trans. Comput. Theory 6(1):1–13.CrossrefGoogle Scholar
  • [27] Panigrahy R (2006) Entropy based nearest neighbor search in high dimensions. Proc. 17th Annual ACM-SIAM Sympos. Discrete Algorithm (Society for Industrial and Applied Mathematics), 1186–1195.Google Scholar
  • [28] Pennington J, Socher R, Manning CD (2014) GLOVE: Global vectors for word representation. Empirical Methods in Natural Language Processing (Association for Computational Linguistics, Doha, Qatar), 1532–1543.Google Scholar
  • [29] Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. Proc. 20th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM), 701–710.Google Scholar
  • [30] Spring R, Shrivastava A (2017) A new unbiased and efficient class of LSH-based samplers and estimators for partition function computation in log-linear models Preprint, submitted March 15, https://arxiv.org/abs/1703.05160.Google Scholar
  • [31] Sugawara K, Kobayashi H, Iwasaki M (2016) On approximately searching for similar word embeddings. Proc. 54th Annual Meeting Assoc. Comput. Linguistics.Google Scholar
  • [32] Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: Large-scale information network embedding. Proc. 24th Internat. Conf. World Wide Web (International World Wide Web Conferences Steering Committee), 1067–1077.Google Scholar
  • [33] Terasawa K, Tanaka Y (2007) Spherical LSH for approximate nearest neighbor search on unit hypersphere. Workshop Algorithms Data Structures (Springer), 27–38.Google Scholar
  • [34] Wang X, Cui P, Wang J, Pei J, Zhu W, Yang S (2017) Community preserving network embedding. Proc. 31st AAAI Conf. Artificial Intelligence, 203–209.Google Scholar
  • [35] Yang Z, Cohen W, Salakhudinov R (2016) Revisiting semi-supervised learning with graph embeddings. Internat. Conf. Machine Learn., 40–48.Google Scholar
  • [36] Zhang C, Bengio S, Hardt M, Recht B, Vinyals O (2016) Understanding deep learning requires rethinking generalization Preprint, submitted November 10, https://arxiv.org/abs/1611.03530.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.