Transformed Neighborhood Propagation

An enhanced label propagation technique termed transformed neighborhood propagation (TNP) is proposed for semi-supervised learning. In the TNP setting, the processes of constructing weighted similarity graph and propagating label information of the labeled data to unlabeled points are conducted in the transformed feature space. TNP is mainly motivated by a fact that the optimal feature representation Y with possible unfavorable features and noises in the original data X removed by feature learning are more appropriate and accurate for measuring pair wise similarities of samples. To achieve the representation Y, The recent marginal semi-supervised sub-manifold projections is applied, so enhanced inter-class separation and enhanced intra-class compactness are delivered at the same time. The similarity graph is finally constructed based on Y. We also propose to calculate semi-supervised reconstruction weights for the weight assignment. As a result, the label estimation power can be enhanced by benefiting from the refined weighted similarity graph over Y instead of X, through propagating the labels of points in the transformed space for prediction. Visualization and image classification verified the effectiveness of our TNP, compared with other related label propagation algorithms.