Real-Valued Embeddings and Sketches for Fast Distance and Similarity Estimation

被引：8
|
作者：
Rachkovskij, D. A. [1 ,2 ]
机构：
[1] NAS, Int Sci Educ Ctr Informat Technol & Syst, Kiev, Ukraine
[2] MON Ukraine, Int Sci Educ Ctr Informat Technol & Syst, Kiev, Ukraine
来源：
CYBERNETICS AND SYSTEMS ANALYSIS | 2016年 / 52卷 / 06期
关键词：
distance; similarity; embedding; sketch; dimensionality reduction; random projection; sampling; Johnson-Lindenstrauss lemma; kernel similarity; similarity search;
D O I：
10.1007/s10559-016-9899-x
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This survey article considers methods and algorithms for fast estimation of data distance/similarity measures from formed real-valued vectors of small dimension. The methods do not use learning and mainly use random projection and sampling. Initial data are mainly high-dimensional vectors with different measures of distance (Euclidean, Manhattan, statistical, etc.) and similarity (dot product, etc.). Vector representations of non-vector data are also considered. The resultant vectors can also be used in similarity search algorithms, machine learning, etc.
引用
收藏
页码：967 / 988
页数：22
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