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 ...

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: Xiao Ling, Wenyuan Dai, Gui-Rong Xue, Qiang Yang, Yong Yu
• Keywords: Data Mining, In-domain Data, KDD 2008, Spectral Classification Framework, Unlabeled Data