We present a graph-based classification approach called sparse graph-based inductive learning (SGIL). Different to the conventional graph-based classifiers, which perform the classification in a semisupervised way, SGIL is a purely supervised method whose classifier is totally learned in an inductive fashion instead of transductive fashion. Similar to the idea of sparse graph-based classifier, SGIL constructs a sparse graph to encode the correlations of training samples, and considers the classification issue as a regularized sparse graph partition issue where the optimal graph cut should not only minimize the correlation loss of the training samples but also minimize the classification errors. Essentially, the learned graph cut plays a role as the predicted labels here. Thus, a linear classifier can be inductively derived by learning a mapping between the training samples and the graph cuts. Since SGIL is purely supervised, it enjoys several desirable properties over the semisupervised ones in graph construction and model training. We evaluate our work on several popular image datasets. The experimental results demonstrate its superiority.