Abstract
Similarity search, translating into the nearest neighbor search problem, finds many applications for information retrieval and visualization, machine learning and data mining. The large volume of data that typical applications should handle imposes to find approximate solutions for the similarity search problem. Permutation-based indexing is one of the most recent techniques for approximate similarity search. Objects are represented by lists ordering their distances to a set of selected reference objects, following the idea that two neighboring objects have the same surrounding. In this paper, we propose a quantized representation of the permutation lists with its related data structure for effective retrieval. Our novel permutation-based indexing strategy is built to be fast, memory efficient and scalable without excessively sacrificing on search precision. This is experimentally demonstrated in comparison to existing proposals using several large-scale dataset of millions of documents and different dimensions.
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Mohamed, H., Marchand-Maillet, S. (2013). Quantized Ranking for Permutation-Based Indexing. In: Brisaboa, N., Pedreira, O., Zezula, P. (eds) Similarity Search and Applications. SISAP 2013. Lecture Notes in Computer Science, vol 8199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41062-8_11
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DOI: https://doi.org/10.1007/978-3-642-41062-8_11
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