System and method for domain adaptation using marginalized stacked denoising autoencoders with domain prediction regularization

A system comprising:

• a domain predictor for predicting a domain of a plurality of domains for a sample, the sample including values for a set of features, the plurality of domains including at least one source domain and a target domain;
• a mapping component which receives a set of training samples from the plurality of domains, each training sample including values for the set of features, the mapping component performing domain adaptation using marginalized denoising autoencoding on the set of training samples to generate at least one domain adaptation transform layer operative to transform the training samples to a common adapted feature space, the domain adaptation incorporating the domain predictor to bias the domain adaptation of the source domain training samples towards one of the plurality of domains;
• and
• a processor which implements the mapping component.

A classification method comprising:

• receiving an input sample from a source domain, the input sample including values for a set of features;
• classifying the input sample with a classifier trained on a set of domain adapted training samples, the domain adapted training samples each including values for the set of features;
• the domain adapted training samples having been generated from a set of training samples, from a plurality of domains including a source domain and a target domain, that have been transformed to an adapted feature space using a marginalized denoising autoencoding layer incorporating a domain predictor, whereby the transformation of source domain target samples is biased towards the target domain such that the domain adapted source domain target samples are more likely to be predicted to be target domain samples by the domain predictor;
• and
• outputting a label prediction for the input sample based on the classification.