A Tribute to the SVD
It was a love at first sight. The Singular Value Decomposition-SVD came into my (research) life back in 2002 as a crucial step of a whole family of Multivariate Data Analysis methods, such as Correspondence Analysis, Principal Component Analysis, Multidimensional Scaling and other. Most of us take advantage of its nice geometric properties as a “black box”, since it’s an important factorization technique with a wide spectrum of applications in statistics and machine learning.
I was wondering how many research articles mention the term SVD in a time period from 1970 to 2008, so I did a series of searches in Google Scholar. SVD gains more and more attention (see Figure below), especially after 1990, with a boom during the 00s. However, I can’t explain the fall in 2008 (I bet it’s not statistically significant :p). How long can the SVD stand the competition of more efficient methods? Will see.
SVD Search Results in Google Scholar
[read more at Wikipedia]
Popularity: 32% [?]
I think that SVD is so well-known and common by now that people may actually use it somewhere in their code, without finally saying so in their paper. Something like part of the standard toolkit. Not bad for a mathematical method…