A Novel Transfer Metric Learning Approach Based on Multi-Group
| Autor: | Zhen Zhu, Pengshuai Yin, Huaqing Min, Bo Xu, Yonghui Xu, Jingtang Zhong, Chang'an Yi |
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| Rok vydání: | 2018 |
| Předmět: |
Mahalanobis distance
Computer science business.industry Feature extraction Cognitive neuroscience of visual object recognition Pattern recognition 02 engineering and technology Handwriting recognition 020204 information systems 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Artificial intelligence business Transfer of learning Knowledge transfer Classifier (UML) |
| Zdroj: | ROBIO |
| DOI: | 10.1109/robio.2018.8665258 |
| Popis: | In recent years, transfer learning receives increasingly attention ranging from the communities of developmental robots, computer vision to artificial intelligence. In the research of transfer learning, knowledge should be transferred from the source domain to the target domain. The source domain is used to train a classifier while the target domain is for testing. Existing works consider the source domain as a whole, however, samples in the source domain might be extracted into different groups and the samples in the same group would have similar intrinsic attributes. In this work, we propose a novel transfer metric learning framework based on multi-group, called TMLMG. In TMLMG, based on each group both a Mahalanobis distance metric and a basic classifier are learned to make predictions. A weight matrix is used to describe the prediction capabilites of all the combinations of groups and Mahalanobis distance metrics. The weight matrix is initialized and optimized based on the labeled samples in the target domain. Experimental results on publicly available datasets of object recognition and handwriting recognition verify the effectiveness of our proposed TMLMG in knowledge transfer. |
| Databáze: | OpenAIRE |
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