Title: TLR: Transfer Latent Representation for Unsupervised Domain Adaptation,
Abstract: Domain adaptation refers to the process of learning prediction models in a
target domain by making use of data from a source domain. Many classic methods
solve the domain adaptation problem by establishing a common latent space,
which may cause the loss of many important properties across both domains. In
this manuscript, we develop a novel method, transfer latent representation
(TLR), to learn a better latent space. Specifically, we design an objective
function based on a simple linear autoencoder to derive the latent
representations of both domains. The encoder in the autoencoder aims to project
the data of both domains into a robust latent space. Besides, the decoder
imposes an additional constraint to reconstruct the original data, which can
preserve the common properties of both domains and reduce the noise that causes
domain shift. Experiments on cross-domain tasks demonstrate the advantages of
TLR over competing methods.