Conference Publication Details
Mandatory Fields
QasemiZadeh, Behrang; Handschuh, Siegfried
25th International Workshop on Database and Expert Systems Applications
Random Manhattan Indexing
In Press
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
Grant Details
Grant Number SFI/12/RC/2289
Publication Themes