Variational Relevance Vector Machine for Tabular Data
Dmitry Kropotov (Dorodnicyn Computing Centre), Dmitry Vetrov
(Lomonosov Moscow State University), Lior Wolf (Tel Aviv University),
and Tal Hassner (The Open University of Israel);
JMLR W&P 13:79-94, 2010.
Abstract
We adopt the Relevance Vector Machine (RVM) framework to handle
cases of table-structured data such as image blocks and image descriptors.
This is achieved by coupling the regularization coefficients
of rows and columns of features. We present two variants of this new
gridRVM framework, based on the way in which the regularization
coefficients of the rows and columns are combined. Appropriate variational
optimization algorithms are derived for inference within this
framework. The consequent reduction in the number of parameters
from the product of the table's dimensions to the sum of its dimensions
allows for better performance in the face of small training sets,
resulting in improved resistance to overfitting, as well as providing
better interpretation of results. These properties are demonstrated
on synthetic data-sets as well as on a modern and challenging visual
identification benchmark.
