Semi-Supervised SVM with Extended Hidden Features

Many traditional semi-supervised learning algorithms not only train on the labeled samples but also incorporate the unlabeled samples in the training sets through an automated labeling process such as manifold preserving. If some labeled samples are falsely labeled, the automated labeling process will generally propagate negative impact on the classifier in quite a serious manner. In order to avoid such an error propagating effect, the unlabeled samples should not be directly incorporated into the training sets during the automated labeling strategy. In this paper, a new semi-supervised support vector machine with extended hidden features (SSVM-EHF) is presented to address this issue. According to the maximum margin principle and the minimum integrated squared error between the probability distributions of the labeled and unlabeled samples, the dimensionality of the labeled and unlabeled samples is extended through an orthonormal transformation to generate the corresponding hidden features shared by the labeled and unlabeled samples. After doing so, the last step in the process of training of SSVM-EHF is done only on the labeled samples with their original and hidden features, and the unlabeled samples are no longer explicitly used. Experimental results confirm the effectiveness of the proposed method.