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Active Information Fusion |
Learning Model Parameters When Data Are Incomplete Given a
graphical model, a serious issue is how to automatically learn the model
parameters from available training data. When a significant amount of data
are missing, or when multiple hidden nodes exist, which is common for many
real-world applications, learning parameters in probabilistic graphical
models becomes extremely difficult, due to the large search space and the
presence of many local maxima. Various learning algorithms are proposed to
address this challenge. However, most of them fail to exploit the
available domain knowledge, which is critical to regularize this otherwise
ill-posed problem. This motivates us to propose a learning algorithm that
overcomes the limitations of the existing methods by systematically
incorporating the prior knowledge into the learning process. Instead of
using the likelihood function as the objective to maximize during
learning, we define the objective function as a combination of the
likelihood function and the penalty functions constructed from the domain
knowledge. Then, a gradient-descent procedure is systematically integrated
with the E-step and M-step of the EM algorithm, to estimate the parameters
iteratively until it converges. The learning algorithms have been applied
to several applications including facial action unit recognition,
human affective state recogntioin, and battlefield stituation assessment.
The experiments showed our algorithm can improve the accuracy of the
learned BN parameters significantly over the conventional EM algorithm.
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