随机曼哈顿索引

B. Zadeh, S. Handschuh
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引用次数: 10

摘要

向量空间模型(vsm)是在数学上定义良好的框架,已广泛应用于文本处理。在这些模型中,高维的、通常稀疏的向量表示文本单元。在应用程序中,向量的相似性——以及它们所代表的文本单位——是通过距离公式计算的。然而,向量的高维是使用向量向量模型的方法性能的一个障碍。因此,采用降维技术来缓解这一问题。本文介绍了一种构建L1规范降维vsm的新方法——随机曼哈顿索引(RMI)。RMI将VSM的构建和降维结合到一个增量的、可扩展的过程中。为了达到目标,RMI采用了稀疏柯西随机投影。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Random Manhattan Indexing
Vector space models (VSMs) are mathematically well-defined frameworks that have been widely used in text processing. In these models, high-dimensional, often sparse vectors represent text units. In an application, the similarity of vectors -- and hence the text units that they represent -- is computed by a distance formula. The high dimensionality of vectors, however, is a barrier to the performance of methods that employ VSMs. Consequently, a dimensionality reduction technique is employed to alleviate this problem. This paper introduces a new method, called Random Manhattan Indexing (RMI), for the construction of L1 normed VSMs at reduced dimensionality. RMI combines the construction of a VSM and dimension reduction into an incremental, and thus scalable, procedure. In order to attain its goal, RMI employs the sparse Cauchy random projections.
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