Memory Vectors for Similarity Search in High-Dimensional Spaces
نویسندگان
چکیده
منابع مشابه
Similarity Search in High-Dimensional Data Spaces
This paper summarizes analytical and experimental results for the nearest neighbor similarity search problem in high-dimensional vector spaces using some kind of space-or data-partitioning scheme. Under the assumptions of uniformity and independence of data, we are able to formally show and to demonstrate that conventional approaches to the nearest neighbor problem degenerate if the dimensional...
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Extended Abstract In recent years, multimedia content-based retrieval has become an important research problem. In order to provide effective and also efficient access to relevant data stored in large (often distributed) digital repositories, advanced software tools are necessary. Content-based retrieval works on the idea of abstracting the contents of an object, for example color or shape in t...
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For similarity search in high-dimensional vector spaces (or ‘HDVSs’), researchers have proposed a number of new methods (or adaptations of existing methods) based, in the main, on data-space partitioning. However, the performance of these methods generally degrades as dimensionality increases. Although this phenomenon-known as the ‘dimensional curse’-is well known, little or no quantitative a.n...
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ژورنال
عنوان ژورنال: IEEE Transactions on Big Data
سال: 2018
ISSN: 2332-7790,2372-2096
DOI: 10.1109/tbdata.2017.2677964