Statistical and Geometrical Alignment using Metric Learning in Domain Adaptation

Domain adapted machine learning is driven by the possibilities of learning from source data distribution to understand different target data distributions. An assumption is made that one application (source) domain always has enough labeled information, but the other related application (target) may contain information that is partially labeled or completely unlabeled. Therefore, it is necessary to train the target domain classifier using enough labeled information of the source domain. However, contrary to primitive assumptions, the source domain and target domain data need not have the same distribution. Therefore, we can't directly use data of source domain to train classifier for data of target domain. Existing approaches can be deprived of one or more objectives: perform geometric diffusion on the manifold, align the cross-domain distributions, preserve the discriminative information using metric learning. Here, we have proposed a novel framework that aims to meet all such objectives. In this framework, we proposed two methods, statistical and geometrical alignment using metric learning with pseudo labels (SGA-MDAP) and without pseudo labels (SGA-MDA) in visual domain adaptation. It has been demonstrated through various experiments that our framework outperforms various state-of-the-art methods over four different real-world cross-domain visual identification datasets such as PIE face, ORL face, Yale face, and Office Caltech.