Regularized Wasserstein Means for Aligning Distributional Data
Autor: | Yalin Wang, Liang Mi, Wen Zhang |
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Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
FOS: Computer and information sciences
Mathematical optimization Computer Science - Machine Learning Property (programming) Computer science Perspective (graphical) Machine Learning (stat.ML) Point set registration General Medicine Sparse approximation Discrete measure Article Machine Learning (cs.LG) 030218 nuclear medicine & medical imaging Domain (software engineering) 03 medical and health sciences 0302 clinical medicine Robustness (computer science) Statistics - Machine Learning Scalability 030217 neurology & neurosurgery |
Zdroj: | AAAI Proc Conf AAAI Artif Intell |
Popis: | We propose to align distributional data from the perspective of Wasserstein means. We raise the problem of regularizing Wasserstein means and propose several terms tailored to tackle different problems. Our formulation is based on the variational transportation to distribute a sparse discrete measure into the target domain. The resulting sparse representation well captures the desired property of the domain while reducing the mapping cost. We demonstrate the scalability and robustness of our method with examples in domain adaptation, point set registration, and skeleton layout. |
Databáze: | OpenAIRE |
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