Domain generalization based on transfer component analysis
|Autoren|| Thomas Grubinger|
|Editoren|| I. Rojas|
|Titel||Domain generalization based on transfer component analysis|
|Buchtitel||Advances in Computational Intelligence - Proc. IWANN 2015, Part I|
|Serie||Lecture Notes in Computer Science|
This paper investigates domain generalization: How to use knowledge acquired from related domains and apply it to new domains? Transfer Component Analysis (TCA) learns a shared subspace by minimizing the dissimilarities across domains, while maximally preserving the data variance. We propose Multi-TCA, an extension of TCA to multiple domains as well as Multi-SSTCA, which is an extension of TCA for semi-supervised learning. In addition to the original application of TCA for domain adaptation problems, we show that Multi-TCA can also be applied for domain generalization. Multi-TCA and Multi-SSTCA are evaluated on two publicly available datasets with the tasks of landmine detection and Parkinson telemonitoring. Experimental results demon-strate that Multi-TCA can improve predictive performance on previously unseen domains.