Abstract
Fast nearest neighbor search (NNS) is becoming important to utilize massive data. Recent work shows that hash learning is effective for NNS in terms of computational time and space. Existing hash learning methods try to convert neighboring samples to similar binary codes, and their hash functions are globally optimized on the data manifold. However, such hash functions often have low resolution of binary codes; each bucket, a set of samples with same binary code, may contain a large number of samples in these methods, which makes it infeasible to obtain the nearest neighbors of given query with high precision. As a result, existing methods require long binary codes for precise NNS. In this paper, we propose Locally Optimized Hashing to overcome this drawback, which explicitly partitions each bucket by solving optimization problem based on that of Spectral Hashing with stronger constraints. Our method outperforms existing methods in image and document datasets in terms of quality of both the hash table and query, especially when the code length is short.
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References
Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: Liblinear: A library for large linear classification. Journal of Machine Learning Research 9, 1871–1874 (2008)
Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the 30th Annual ACM Symposium on Theory of Computing, pp. 604–613 (1998)
Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing for scalable image search. In: IEEE 12th International Conference on Computer Vision, pp. 2130–2137 (2009)
Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: A new benchmark collection for text categorization research. Journal of Machine Learning Research 5, 361–397 (2004)
Li, P., König, A.C.: b-Bit minwise hashing. In: Proceedings of the 19th International Conference on World Wide Web, pp. 671–680 (2010)
Liu, W., Wang, J., Kumar, S., Chang, S.-F.: Hashing with graphs. In: Proceedings of the 28th International Conference on Machine Learning, pp. 1–8 (2011)
Norouzi, M., Fleet, D.: Minimal loss hashing for compact binary codes. In: Proceedings of the 28th International Conference on Machine Learning, pp. 353–360 (2011)
Raginsky, M., Lazebnik, S.: Locality-sensitive binary codes from shift-invariant kernels. In: Advances in Neural Information Processing Systems, vol. 22, pp. 1509–1517 (2009)
Wang, J., Kumar, S., Chang, S.-F.: Sequential projection learning for hashing with compact codes. In: Proceedings of the 27th International Conference on Machine Learning, pp. 1127–1134 (2010)
Watkins, D.S.: Fundamentals of Matrix Computations. Pure and Applied Mathematics: A Wiley Series of Texts, Monographs and Tracts. Wiley, third edition edition (2010)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advances in Neural Information Processing Systems, vol. 21, pp. 1753–1760 (2009)
Zhang, D., Wang, J., Cai, D., Lu, J.: Self-taught hashing for fast similarity search. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 18–25 (2010)
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Tokui, S., Sato, I., Nakagawa, H. (2015). Locally Optimized Hashing for Nearest Neighbor Search. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9078. Springer, Cham. https://doi.org/10.1007/978-3-319-18032-8_39
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DOI: https://doi.org/10.1007/978-3-319-18032-8_39
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