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Inductive manifold learning using structured support vector machine
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DOI：10.1016/j.patcog.2013.07.011.png)
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Abstract
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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. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Dimensionality reduction
Manifold learning
Out-of-sample extension
Structured SVM
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