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Random Manhattan Indexing

Authors: 

Behrang QasemiZadeh, Siegfried Handschuh

Publication Type: 
Refereed Conference Meeting Proceeding
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.
Proceedings: 
Database and Expert Systems Applications (DEXA), 25th International Workshop on
Digital Object Identifer (DOI): 
10.na
Publication Date: 
04/09/2014
Conference Location: 
Germany
Research Group: 
Institution: 
National University of Ireland, Galway (NUIG)
Open access repository: 
Yes
Publication document: