Transductive Bayesian regression via manifold learning of prior data structure |
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| Authors: | Hyejin Park Heun A Kim Seung-ho Yang Jaewook Lee |
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| Affiliation: | 1. Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), San 31 Hyoja, Pohang 790-784, South Korea;2. Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), San 31 Hyoja, Pohang 790-784, South Korea;3. Department of Industrial Engineering, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul 151-744, South Korea;1. Department of Materials Science and Engineering, College of Engineering, Seoul National University, Daehak-dong, Gwanak-gu, Seoul 151-744, Republic of Korea;2. Department of Mechanical Engineering, Stanford University, CA 94305, USA;3. Department of Materials Science and Engineering, Ajou University, Woncheon-dong, San 5, Yeongtong-gu, Suwon 443-749, Republic of Korea;1. School of Chemical and Biological Engineering, Seoul National University, Seoul 151-742, Republic of Korea;2. Department of Chemical Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea;1. Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea;2. Clinical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea;3. Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea;4. Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine, Seoul, Republic of Korea;5. Department of Neuropsychiatry, Dongguk University International Hospital, Dongguk University Medical School, Gyeonggi-do, Republic of Korea;1. DuPont Pioneer, 7200 NW 62nd Avenue, Johnston, IA 50131, USA;2. Electrical and Computer Engineering Department, Iowa State University, Ames, IA 50010, USA;1. Argonne National Laboratory, Energy Systems Division, 9700 S. Cass Ave., Argonne, IL 60439, USA;2. School of Chemical and Biological Engineering and Institute of Chemical Processes, Seoul National University, 1 Gwanangno, Gwanak-gu, Seoul 151-744, Republic of Korea;3. Supercritical Fluid Research Laboratory, Clean Energy Research Center, Korea Institute of Science and Technology, Hwarangno 14-gil 5, Seongbuk-gu, Seoul 136-791, Republic of Korea;4. Graduate School of Convergence Green Technology & Policy, Korea University, 5-1 Anam Dong, Seongbuk-gu, Seoul 136-701, Republic of Korea;1. Department of Neurosurgery, Seoul National University College of Medicine, 28 Yongon-dong, Chongno-gu, Seoul 110–744, Korea;2. Neuro-Oncology Clinic, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do 410-769, Korea |
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| Abstract: | During the last decades, many studies have been conducted on performing reliable prediction for high-dimensional data that are usually non-linearly correlated with complex patterns. In this paper, we propose a novel Bayesian regression method via non-linear dimensionality reduction. The method incorporates prior information on the underlying structure of original input features to preserve input–output patterns on reduced features, and to provide distributions of predicted values. To verify the effectiveness of the proposed method, we conducted simulations on benchmark and real-world data. Results showed that the method not only better predicts a distribution of forecast estimates compared with other methods, but also more robust and consistent performance on prediction. |
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