Topological Data Analysis of Database Representations for Information Retrieval

• URL: http://arxiv.org/abs/2104.01672v1
• Date: Sun, 4 Apr 2021 19:29:47 GMT
• Title: Topological Data Analysis of Database Representations for Information Retrieval
• Authors: Athanasios Vlontzos, Yueqi Cao, Luca Schmidtke, Bernhard Kainz, and Anthea Monod
• Abstract summary: Persistent homology provides a rigorous characterization for the database topology. We show that some commonly used embeddings fail to preserve the connectivity. We introduce the dilation-invariant bottleneck distance to capture this effect.
• Score: 2.729524133721473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Appropriately representing elements in a database so that queries may be accurately matched is a central task in information retrieval. This recently has been achieved by embedding the graphical structure of the database into a manifold so that the hierarchy is preserved. Persistent homology provides a rigorous characterization for the database topology in terms of both its hierarchy and connectivity structure. We compute persistent homology on a variety of datasets and show that some commonly used embeddings fail to preserve the connectivity. Moreover, we show that embeddings which successfully retain the database topology coincide in persistent homology. We introduce the dilation-invariant bottleneck distance to capture this effect, which addresses metric distortion on manifolds. We use it to show that distances between topology-preserving embeddings of databases are small.

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