[2A4-GS-2] Machine learning

[2A4-GS-2-03] Heterogeneous Domain Adaptation with Positive and Unlabeled Data

〇Junki Mori¹, Ryo Furukawa¹, Isamu Teranishi¹, Jun Sakuma^2,3 (1. NEC Secure System Platform Research Laboratories, 2. University of Tsukuba, 3. RIKEN Center for Advanced Intelligence Project)

Keywords:machine learning , domain adaptation, PU learning

Domain adaptation (DA) is a method for learning a model with good performance for target data, given labeled source data and unlabeled target data from different domains, by learning a domain-invariant feature space. Heterogeneous domain adaptation (HDA) is a type of DA that can be applied when the feature space differs between the source and target data. Conventional HDA assumes that all labels exist in the source data, but in reality there are cases where only positive examples exist. In this paper, we propose a HDA method in such a setting, i.e., in a PU learning setting where only positive source data and unlabeled target data exist. By using adversarial learning, the proposed method simultaneously achieves binary classification of positive and negative examples in the target data and learning of domain-invariant feature space. We experimentally show that the proposed method outperforms the performance of various baseline methods.