Abstract
The Distance Index (D-index) is a recently introduced metric indexing structure which has state-of-the-art performance in large scale metric search applications. Inspired by D-index, we introduce a novel index structure, termed AdaIndex, for fast similarity search in generic metric spaces. With multiple principles from other advanced algorithms, AdaIndex shows a significant improvement in reduction of distance calculations compared with D-index. To treat with application with different system limitations and diverse nature of data, we introduce a parameter tuning algorithm to build an optimal AdaIndex structure with minimal overall computational costs. The efficiency of AdaIndex is validated on a series of simulation experiments.
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© 2009 Springer-Verlag Berlin Heidelberg
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Ban, T., Guo, S., Xu, Q., Kadobayashi, Y. (2009). AdaIndex: An Adaptive Index Structure for Fast Similarity Search in Metric Spaces. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_81
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DOI: https://doi.org/10.1007/978-3-642-10684-2_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10682-8
Online ISBN: 978-3-642-10684-2
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