Inductive manifold learning using structured support vector machine

• We propose a general framework for out-of-sample extensions for any manifold learning methods.
• We learn a mapping from original space to its manifolds by using structured SVM.
• Experiments on several datasets show that the proposed method outperforms the existing ones.

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.