High dimensional graphical model for categorical variables
by Zhao Yuqi
THESIS
2017
M.Phil. Mathematics
x, 32 pages : illustrations (some color) ; 30 cm
Abstract
We propose a graphical model associated with categorical variables and study the
problem of structure learning for this model. The model is a natural generalization
of Ising model and the learning method is based on neighborhood selection by multinomial
logistics regression. Group lasso was incorporated into the regression since
parameters representing interaction between variables have group structure. The
performance of proposed method is studied by simulations, and we apply this model
to the music annotation dataset CAL500 to obtain the graphical model for music
labels.
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