Minwise-Independent Permutations with Insertion and Deletion of Features
Pages 171 - 184
Abstract
The seminal work of Broder et al. [5] introduces the algorithm that computes a low-dimensional sketch of high-dimensional binary data that closely approximates pairwise Jaccard similarity. Since its invention, has been commonly used by practitioners in various big data applications. In many real-life scenarios, the data is dynamic and their feature sets evolve over time. We consider the case when features are dynamically inserted and deleted in the dataset. A naive solution to this problem is to repeatedly recompute with respect to the updated dimension. However, this is an expensive task as it requires generating fresh random permutations. To the best of our knowledge, no systematic study of is recorded in the context of dynamic insertion and deletion of features. In this work, we initiate this study and suggest algorithms that make the sketches adaptable to the dynamic insertion and deletion of features. We show a rigorous theoretical analysis of our algorithms and complement it with supporting experiments on several real-world datasets. Empirically we observe a significant speed-up in the running time while simultaneously offering comparable performance with respect to running from scratch. Our proposal is efficient, accurate, and easy to implement in practice.
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Oct 2023
324 pages
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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Published: 27 October 2023
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