Algorithm 2 PVM Using the Hinge Loss
1:	Perform k-means clustering on the given labeled and unlabeled samples, and use the cluster centers as prototypes.
2:	Use (17) to compute H (and thus obtain its submatrices Hl and Hu).
3:	Use the approximate graph Laplacian [(13) or (14)] to compute A in (24), and subsequently Q in (28).
4:	Solve the QP in (27) to obtain β.
5:	Obtain ${\mathbf{f}}_{\upsilon }^{\ast }$ from (29).
6:	Prediction
	1) on the given unlabeled samples: $sgn({\mathbf{f}}_u)=sgn({\mathbf{H}}_u{\mathbf{f}}_{\upsilon }^{\ast })$
	2) on an unseen $\mathbf{x}:sgn([K(\mathbf{x},{\mathbf{v}}_1),\dots ,K(\mathbf{x},{\mathbf{v}}_r)]{\mathbf{f}}_{\upsilon }^{\ast })$.