Semi-Supervised Dimensionality Reduction and Classification through Virtual Label Regression

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Authors Feiping Nie, Dong Xu, Xuelong Li, Shiming Xiang
Journal/Conference Name IEEE Transactions on Systems, Man and Cybernetics, Part B (TSMCB)
Paper Category
Paper Abstract Semi-supervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semi-supervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled data to unlabeled data with a well designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this mean, when the manifold assumption or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data, and thus the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases and the advantage of the new approach is well demonstrated.
Date of publication 2011
Code Programming Language MATLAB
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