Sampling Methods for Random Subspace Domain Adaptation

Supervised classification tasks like Sentiment Analysis or text classification need labelled training data. These labels can be difficult to obtain, especially for complicated and ambiguous data like texts. Instead of labelling new data, domain adaptation tries to reuse already labelled data from related tasks as training data. We propose a greedy selection strategy to identify a small subset of data samples that are most suited for domain adaptation. Using these samples the adaptation is done on a subspace in a kernel defined feature space. To make this kernel approach applicable for large scale data sets, we use random Fourier features to approximate kernels by expectations.