Sparse Domain Adaptation in Projection Spaces based on Good Similarity Functions

5 Modifying the Projection Space for Domain Adaptation In this section, we present our two approaches for modifying the similarity based projection space in order to facilitate

Hence, as pointed out in [2], Equation (1) together with the usual VC-bound theory, express a multiple trade-off between the accuracy of some particular hypothesis h, the complexity

For this purpose, we have investigated the algorithmic robustness notion proposed by Xu and Mannor [3] based on the following property: “If a testing sample is similar to a

The introduced ap- proach, Joint Class Proportion and Optimal Transport (JCPOT), performs multi-source adaptation and target shift correction simul- taneously by learning the

The obtained results contain a divergence term between the two domains distributions that naturally appears when bounding the deviation between the same similarity’s performance on

Since, we may consider non-PSD, non-symmetric similarity functions and no label on the target domain, we then make a little adaptation: We perform the reverse valida- tion directly

Finally, the multi-class case and the semi-supervised domain adaptation (i.e when some labels are available on the target) are cru- cial extensions to add as they would allow us to

We derive from this bound a novel adversarial domain adaptation algorithm adjusting weights given to each source, ensuring that sources related to the target receive higher weights..