Well said by John Dupuis.

Popularity: 25% [?]

Suppose this is a blog, traditionally speaking; a diary, a place to keep notes and thoughts on past/upcoming events, my character and life lessons I’ve learned. At the end, add vanity (ματαιοδοξία, in Greek) as an important ingredient, otherwise, I’d have written down everything in a diary and kept it locked in a drawer.

After this prologue I can safely present some notes on my upcoming research in the field of (mostly Multivariate) Data Analysis:

I plan to…

• …further study the theoretical aspects of Correspondence Analysis and Related Methods, or simply CARME and particularly examine the parametrization which embeds different methods into a common super-family, which according to M. Greenacre can be useful as a tuning parameter in supervised learning if there is an outcome variable.
• …analyze, explore and interpret data from social sciences and humanities, as well as from machine learning approaches, such as Collaborative Filtering.
• …develop data analysis software that is reasonably comprehensive, fairly easy to use and free as in freedom. After the CHIC Analysis software, I’m experimenting with Tcl/Tk and R in order to build a package which implements a graphical user interface for the ca package of Nenadic & Greenacre.

Not boring enough?

Popularity: 26% [?]

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% [?]