Classification of handwritten digits using supervised locally linear embedding algorithm and support vector machine

The locally linear embedding (LLE) algorithm is an unsupervised technique recently proposed for nonlinear dimensionality reduction. In this paper, we describe its supervised variant (SLLE). This is a conceptually new method, where class membership information is used to map overlapping high dimensional data into disjoint clusters in the embedded space. In experiments, we combined it with support vector machine (SVM) for classifying handwritten digits from the MNIST