Manifold embedded joint geometrical and statistical alignment for visual domain adaptation

Supervised learning algorithms like KNN assume that the training and the testing data come from the same source, and hence, have the same distribution. However, in real life, such instances are rare. Domain adaptation (DA) aims to model a classifier that is invariant to the different features of the training data. Domain invariant features are utilized to reduce distribution differences, ensuring an iterative improvement on the predicted target labels. Existing DA approaches either perform manifold subspace learning or align cross-domain distributions. At the same time, there exists a couple of imperative limitations: (1) degenerated feature transformation, wherein the seemingly domain invariant features are distorted during transformation (2) formulation of a robust objective function that must consider critical objectives in a unified framework to minimize deviations geometrically and statistically. To the best of our knowledge, none of the existing DA approaches address these two limitations simultaneously. Therefore, we propose a novel domain adaptation framework, called Manifold Embedded Joint Geometrical and Statistical Alignment (MEJGSA) for visual domain adaptation to address these limitations. MEJGSA first learns manifold features, and then formulates a robust objective function to reduce divergence between domains geometrically and statistically. We also consider more reliable classifiers to generate pseudo-labels to preserve objective functions such as conditional distributions and target domain discriminant information. Extensive experiments on two widely used cross-domain adaptation datasets demonstrate that MEJGSA exhibits significant improvements in classification accuracy as compared to cutting-edge shallow and deep DA approaches.