Recognizing heterogeneous cross domain data via generalized joint distribution adaptation
In this paper, we propose a novel algorithm of Generalized Joint Distribution Adaptation (G JDA) for heterogeneous domain adaptation (HDA) which associates and recognizes cross domain data observed in different feature spaces (and thus with different dimensionality) With the objective to derive a domain invariant feature subspace for relating source and target domain data, our G JDA learns a pair of feature projection matrices (one for each domain) which allows us to eliminate the difference between projected cross domain heterogeneous data by matching their marginal and class conditional distributions. We conduct experiments on cross domain classification tasks using data across different features, datasets, and modalities. We confirm that our G JDA would perform favorably against state of the art HDA approaches.
2016 IEEE International Conference on Multimedia and Expo (ICME)