Data　labeling　is　a　tedious　and　subjective　task　that　can　be　time　consuming　and　error-prone;　however,　most　learning　algorithms　are　sensitive　to　noisy　labels.　This　problem　raises　the　need　to　develop　algorithms　that　can　exploit　large　amount　of　unlabeled　data　and　also　be　robust　to　noisy　label　information.　In　this　letter,　we　propose　a　novel　semi-supervised　classification　framework　that　is　robust　to　noisy　labels,　named　self-paced　manifold　regularization.　The　proposed　framework　naturally　integrates　self-paced　learning　regime　into　the　manifold　regularization　framework　for　selecting　labeled　training　samples　in　a　theoretically　sound　manner,　and　utilizes　locally　linear　reconstructions　to　control　the　smoothness　of　the　classifier　with　respect　to　the　manifold　structure　of　data.　Finally,　the　alternative　search　strategy　is　adopted　for　the　proposed　framework　to　obtain　the　classifier.　The　proposed　method　can　not　only　suppress　the　negative　effect　of　noisy　initial　labels　in　semi-supervised　learning,　but　also　obtain　an　explicit　multiclass　classifier　for　newly　coming　data　points.　Experimental　results　demonstrate　the　effectiveness　of　the　proposed　method.