SRS: solving c -approximate nearest neighbor queries in high dimensional euclidean space with a tiny index
Year: 2014
Type: article
Abstract: Nearest neighbor searches in high-dimensional space have many important applications in domains such as data mining, and multimedia databases. The problem is challenging due to the phenomenon called "curse of dimensionality". An alternative solution is to consider algorithms that returns a c -approximate nearest neighbor ( c -ANN) with guaranteed probabilities. Locality Sensitive Hashing (LSH) is among the most widely adopted method, and it achieves high efficiency both in theory and practice. However, it is known to require an extremely high amount of space for indexing, hence limiting its scalability. In this paper, we propose several surprisingly simple methods to answer c -ANN queries with theoretical guarantees requiring only a single tiny index. Our methods are highly flexible and support a variety of functionalities, such as finding the exact nearest neighbor with any given probability. In the experiment, our methods demonstrate superior performance against the state-of-the-art LSH-based methods, and scale up well to 1 billion high-dimensional points on a single commodity PC.
Institutions: UNSW Sydney, University of Technology Sydney
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Real-time query processing over multi-dimensional uncertain data streams, G3800025572, G4611444012, Effective and efficient record linkage with transformation rules, Taming the uncertainty in trajectory data, Probabilistic search over large-scale uncertain graphs, Ranking complex objects in a multi-dimensional space, Next-generation spatial keyword search
OpenAlex ID: W642889137