document: Spectral domain-transfer learning Traditional spectral classification has been proved to be effective in dealing with both labeled and unlabeled data when these data are from the same domain . In many real world applications , however , we wish to make use of the labeled data from one domain ( called in-domain ) to classify the unlabeled data in a different domain ( out-of-domain ) . This problem often happens when obtaining labeled data in one domain is difficult while there are plenty of labeled data from a related but different domain . In general , this is a transfer learning problem where we wish to classify the unlabeled data through the labeled data even though these data are not from the same domain . In this paper , we formulate this domain-transfer learning problem under a novel spectral classification framework , where the objective function is introduced to seek consistency between the in-domain supervision and the out-of-domain intrinsic structure . Through optimization of the cost function , the label information from the in-domain data is effectively transferred to help classify the unlabeled data from the out-of-domain . We conduct extensive experiments to evaluate our method and show that our algorithm achieves significant improvements on classification performance over many state-of-the-art algorithms .
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