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Quantized ranking for permutation-based indexing
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DOI：10.1016/j.is.2015.01.009.png)
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Abstract
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The K-Nearest Neighbor (K-NN) search problem is the way to find the K closest and most similar objects to a given query. The K-NN is essential for many applications such as information retrieval and visualization, machine learning and data mining. The exponential growth of data imposes to find approximate approaches to this problem. Permutation-based indexing is one of the most recent techniques for approximate similarity search. Objects are represented by permutation 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 novel quantized representation of permutation lists with its related data structure for effective retrieval on single and multicore architectures. Our novel permutation-based indexing strategy is built to be fast, memory efficient and scalable. This is experimentally demonstrated in comparison to existing proposals using several large-scale datasets of millions of documents and of different dimensions. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords:
Large-scale indexing
Permutation-based indexing
Approximate similarity search
Metric permutation table
Quantized ranking
Big-data
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