Inductive manifold learning using structured support vector machine |
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Authors: | Kyoungok Kim Daewon Lee |
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Affiliation: | 1. Department of Industrial and Management Engineering, POSTECH, San 31, Hyoja-Dong, Nam-Gu, Pohang, Kyungbuk 790-784, Republic of Korea;2. School of Industrial Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan 680-749, Republic of Korea |
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Abstract: | Most manifold learning techniques are used to transform high-dimensional data sets into low-dimensional space. In the use of such techniques, after unseen data samples are added to the data set, retraining is usually necessary. However, retraining is a time-consuming process and no guarantee of the transformation into the exactly same coordinates, thus presenting a barrier to the application of manifold learning as a preprocessing step in predictive modeling. To solve this problem, learning a mapping from high-dimensional representations to low-dimensional coordinates is proposed via structured support vector machine. After training a mapping, low-dimensional representations of unobserved data samples can be easily predicted. Experiments on several datasets show that the proposed method outperforms the existing out-of-sample extension methods. |
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Keywords: | Dimensionality reduction Manifold learning Out-of-sample extension Structured SVM |
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