Finding robust transfer features for unsupervised domain adaptation - Gao, Depeng; Wu, Rui; Liu, Jiafeng; Fan, Xiaopeng; Tang, Xianglong (Zielona Góra: Uniwersytet Zielonogórski)
An insufficient number or lack of training samples is a bottleneck in traditional machine learning and object recognition. Recently, unsupervised domain adaptation has been proposed and then widely applied for cross-domain object recognition, which can utilize the labeled samples from a source domain to improve the classification performance in a target domain where no labeled sample is available. The two domains have the same feature and label spaces but different distributions.
Keywords: unsupervised domain adaptation; feature reduction; generalized eigenvalue decomposition; object recognition