  In this paper, we invest the domain transfer learning problem with
multi-instance data. We assume we already have a well-trained multi-instance
dictionary and its corresponding classifier from the source domain, which can
be used to represent and classify the bags. But it cannot be directly used to
the target domain. Thus we propose to adapt them to the target domain by adding
an adaptive term to the source domain classifier. The adaptive function is a
linear function based a domain transfer multi-instance dictionary. Given a
target domain bag, we first map it to a bag-level feature space using the
domain transfer dictionary, and then apply a the linear adaptive function to
its bag-level feature vector. To learn the domain-transfer dictionary and the
adaptive function parameter, we simultaneously minimize the average
classification error of the target domain classifier over the target domain
training set, and the complexities of both the adaptive function parameter and
the domain transfer dictionary. The minimization problem is solved by an
iterative algorithm which update the dictionary and the function parameter
alternately. Experiments over several benchmark data sets show the advantage of
the proposed method over existing state-of-the-art domain transfer
multi-instance learning methods.
