Learning Cluster Representatives for Approximate Nearest Neighbor Search
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912148049887232 |
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| author | Vecchiato, Thomas |
| author_facet | Vecchiato, Thomas |
| contents | Developing increasingly efficient and accurate algorithms for approximate nearest neighbor search is a paramount goal in modern information retrieval. A primary approach to addressing this question is clustering, which involves partitioning the dataset into distinct groups, with each group characterized by a representative data point. By this method, retrieving the top-k data points for a query requires identifying the most relevant clusters based on their representatives -- a routing step -- and then conducting a nearest neighbor search within these clusters only, drastically reducing the search space.
The objective of this thesis is not only to provide a comprehensive explanation of clustering-based approximate nearest neighbor search but also to introduce and delve into every aspect of our novel state-of-the-art method, which originated from a natural observation: The routing function solves a ranking problem, making the function amenable to learning-to-rank. The development of this intuition and applying it to maximum inner product search has led us to demonstrate that learning cluster representatives using a simple linear function significantly boosts the accuracy of clustering-based approximate nearest neighbor search. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05921 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Cluster Representatives for Approximate Nearest Neighbor Search Vecchiato, Thomas Information Retrieval Developing increasingly efficient and accurate algorithms for approximate nearest neighbor search is a paramount goal in modern information retrieval. A primary approach to addressing this question is clustering, which involves partitioning the dataset into distinct groups, with each group characterized by a representative data point. By this method, retrieving the top-k data points for a query requires identifying the most relevant clusters based on their representatives -- a routing step -- and then conducting a nearest neighbor search within these clusters only, drastically reducing the search space. The objective of this thesis is not only to provide a comprehensive explanation of clustering-based approximate nearest neighbor search but also to introduce and delve into every aspect of our novel state-of-the-art method, which originated from a natural observation: The routing function solves a ranking problem, making the function amenable to learning-to-rank. The development of this intuition and applying it to maximum inner product search has led us to demonstrate that learning cluster representatives using a simple linear function significantly boosts the accuracy of clustering-based approximate nearest neighbor search. |
| title | Learning Cluster Representatives for Approximate Nearest Neighbor Search |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2412.05921 |