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- W2011904534 abstract "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." @default.
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- W2011904534 date "2014-09-01" @default.
- W2011904534 modified "2023-09-27" @default.
- W2011904534 title "Phased Vertical Cavity Laser Arrays" @default.
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- W2011904534 doi "https://doi.org/10.1109/islc.2014.234" @default.
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