NEW TOOLS OF CYBERNETICS, INFORMATICS,COMPUTER ENGINEERING, AND SYSTEMS ANALYSIS: BINARY VECTORS FOR FAST DISTANCE AND SIMILARITY ESTIMATION

D. A. Rachkovskij

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NEW TOOLS OF CYBERNETICS, INFORMATICS,COMPUTER ENGINEERING, AND SYSTEMS ANALYSIS: BINARY VECTORS FOR FAST DISTANCE AND SIMILARITY ESTIMATION

机译：网络，信息学，计算机工程和系统分析的新工具：用于快速距离和相似性估计的二进制向量

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This review considers methods and algorithms for fast estimation of distance/similarity measures between initial data from vector representations with binary or integer-valued components obtained from initial data that are mainly high-dimensional vectors with different distance measures (angular, Euclidean, and others) and similarity measures (cosine, inner product, and others). Methods without learning that mainly use random projections with the subsequent quantization and also sampling methods are discussed. The obtained vectors can be applied in similarity search, machine learning, and other algorithms.

机译：这篇综述考虑了用于快速估计矢量表示的初始数据之间的距离/相似性度量的方法和算法，这些矢量表示具有从初始数据获得的二进制或整数值分量，这些分量主要是具有不同距离度量（角，欧几里得等）的高维向量和相似性度量（余弦，内积和其他度量）。讨论了没有学习的方法，该方法主要使用随机投影和随后的量化以及采样方法。所获得的向量可以应用于相似性搜索，机器学习和其他算法中。

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来源
《Cybernetics and Systems Analysis》 |2017年第1期|138-156|共19页
作者
D. A. Rachkovskij;
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作者单位

International Scientific-Educational Center of Information Technologies and Systems, NAS and MES of Ukraine Kyiv, Ukraine;

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收录信息美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
distance; similarity; embedding; sketch; random projection; sampling; binarization; quantization; Johnson-Lindenstrauss lemma; kernel similarity; similarity search; locality-sensitive hashing;

机译：距离;相似;嵌入草图;随机投影采样;二值化量化约翰逊-林登斯特劳斯引理;内核相似度;相似度搜索局部敏感哈希;

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NEW TOOLS OF CYBERNETICS, INFORMATICS,COMPUTER ENGINEERING, AND SYSTEMS ANALYSIS: BINARY VECTORS FOR FAST DISTANCE AND SIMILARITY ESTIMATION

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