TL;DR
This paper explores the concept of intrinsic dimensionality in data, focusing on its implications for similarity search within metric spaces, aiming to improve understanding and efficiency.
Contribution
It introduces a formal discussion of intrinsic dimensionality and its relevance to similarity search in metric spaces, providing foundational insights.
Findings
Intrinsic dimensionality affects similarity search performance.
Metrics can be adapted based on intrinsic data properties.
Understanding dimensionality aids in designing better search algorithms.
Abstract
This entry for the SIGSPATIAL Special July 2010 issue on Similarity Searching in Metric Spaces discusses the notion of intrinsic dimensionality of data in the context of similarity search.
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Taxonomy
TopicsData Management and Algorithms · Advanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques