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Online Optimized Product Quantization for ANN Queries over Dynamic Database using SVD-Updating

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Transactions on Large-Scale Data- and Knowledge-Centered Systems LII

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 13470))

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Abstract

Approximate nearest neighbor (ANN) search allows us to perform similarity search over massive vectors with less memory and computation. Optimized Product Quantization (OPQ) is one of the state-of-the-art methods for ANN where data vectors are represented as combinations of codewords by taking into account the data distribution. However, it suffers from degradation in accuracy when the database is frequently updated with incoming data whose distribution is different. An existing work, Online OPQ, addressed this problem, but the computational cost is high because it requires to perform of costly singular value decomposition for updating the codewords. To this problem, we propose a method for updating the rotation matrix using SVD-Updating, which can dynamically update the singular matrix using low-rank approximation. Using SVD-Updating, instead of performing multiple singular value decomposition on a high-rank matrix, we can update the rotation matrix by performing only one singular value decomposition on a low-rank matrix. In the experiments, we prove that the proposed method shows a better trade-off between update time and retrieval accuracy than the comparative methods.

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Acknowledgments

This paper was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI under Grant Number JP22H03694 and the New Energy and Industrial Technology Development Organization (NEDO) Grant Number JPNP20006.

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Authors and Affiliations

  1. Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan

    Kota Yukawa

  2. Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan

    Toshiyuki Amagasa

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  1. Kota Yukawa
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  2. Toshiyuki Amagasa
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Correspondence to Toshiyuki Amagasa .

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Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. IFS, Technical University of Vienna, Vienna, Austria

    A Min Tjoa

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Yukawa, K., Amagasa, T. (2022). Online Optimized Product Quantization for ANN Queries over Dynamic Database using SVD-Updating. In: Hameurlain, A., Tjoa, A.M. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems LII. Lecture Notes in Computer Science(), vol 13470. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-66146-8_4

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  • DOI: https://doi.org/10.1007/978-3-662-66146-8_4

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  • Online ISBN: 978-3-662-66146-8

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