Conference Publication Details
Mandatory Fields
QasemiZadeh, Behrang; Handschuh, Siegfried
25th International Workshop on Database and Expert Systems Applications
Random Manhattan Indexing
2014
September
In Press
1
()
Optional Fields
vector space model dimensionality reduction random projection Manhattan distance retrieval models
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 anddimension reduction into an incremental, and thus scalable, procedure. In order to attain its goal, RMI employs the sparse Cauchy random projections.
Science Foundation Ireland
http://atmykitchen.info/sites/default/files/publications/tir-rmi.pdf
Grant Details
Grant Number SFI/12/RC/2289
Publication Themes