Speeding Up Latent Semantic Analysis: A Streamed Distributed Algorithm for SVD Updates
| Authors | |
|---|---|
| Year of publication | 2010 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART) |
| MU Faculty or unit | |
| Citation | |
| Web | http://www.icaart.org/Program_Saturday.htm |
| Field | Information theory |
| Keywords | svd lda lsi |
| Description | The purpose of Latent Semantic Analysis (LSA) is to find hidden (latent) structure in a collection of texts represented in the Vector Space Model. LSA was introduced in~\cite{deerwester1990indexing} and has since become a standard tool in the field of Natural Language Processing and Information Retrieval. At the heart of LSA lies the \emph{Singular Value Decomposition} algorithm, which makes LSA (sometimes also called Latent Semantic Indexing, or LSI) really just another member of the broad family of applications that make use of SVD's robust and mathematically well-founded approximation capabilities, from Image Processing; or Signal Processing, where SVD is commonly used to separate signal from noise. SVD is also used in solving shift-invariant differential equations, in Geophysics, in Antenna Array Processing, \ldots}. In this way, although we will discuss our results in the perspective and terminology of LSA and Natural Language Processing, our results are in fact applicable to a wide range of problems and domains across much of the field of Computer Science. |
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