Speeding Up Latent Semantic Analysis: A Streamed Distributed Algorithm for SVD Updates

Varování

Publikace nespadá pod Pedagogickou fakultu, ale pod Fakultu informatiky. Oficiální stránka publikace je na webu muni.cz.
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ŘEHŮŘEK Radim

Rok publikování 2010
Druh Článek ve sborníku
Konference Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART)
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www http://www.icaart.org/Program_Saturday.htm
Obor Teorie informace
Klíčová slova svd lda lsi
Popis 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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