Abstract
This paper proposes an approximate nearest neighbor search algorithm for high-dimensional data. The proposed algorithm is based on a distance-based hashing called adaptive flexible distance-based hashing (AFDH). For a given query, AFDH returns a small-sized candidate set of nearest neighbors, and the one closest to the query is selected as the final result. The main advantage of the proposed algorithm is that, without fine tuning of parameter values of the algorithm, good search results can be obtained. Experimental results show that the proposed algorithm produces satisfactory results in terms of quality of results as well as execution time.
This work was supported by JSPS KAKENHI Grant Number 17K00188.
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References
Athitsos, V., Potamias, M., Papapetrou, P., Kollios, G.: Nearest neighbor retrieval using distance-based hashing. In: Proceedings of IEEE International Conference on Data Engineering, pp. 327–336 (2008)
Biau, G., Devroye, L.: Lectures on the Nearest Neighbor Method. SSDS. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25388-6
He, J., Radhakrishnan, R., Chang, S.-F., Bauer, C.: Compact hashing with joint optimization of search accuracy and time. In: Proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition, pp. 753–760 (2011)
Hinneburg, A., Aggarwal, C.C., Keim, D.A.: What is the nearest neighbor in high dimensional spaces? In: Proceedings of 26th VLDB Conference, pp. 506–515 (2000)
Hwang, Y., Han, B., Ahn, H.-K.: A fast nearest neighbor search algorithm by nonlinear embedding. In: Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3053–3060 (2012)
Muja, M., Lowe, D.G.: Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2227–2240 (2014)
Yianilos, P.N.: Data structures and algorithms for nearest neighbor search in general metric spaces. In: Proceedings of Fourth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 311–321 (1993)
Yiu, M.L., Assent, I., Jensen, C.S., Kalnis, P.: Outsourced similarity search on metric data assets. IEEE Trans. Knowl. Data Eng. 24(2), 338–352 (2012)
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Itotani, Y., Wakabayashi, S., Nagayama, S., Inagi, M. (2018). An Approximate Nearest Neighbor Search Algorithm Using Distance-Based Hashing. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11030. Springer, Cham. https://doi.org/10.1007/978-3-319-98812-2_17
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DOI: https://doi.org/10.1007/978-3-319-98812-2_17
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