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.