A Collaborative Deep and Shallow Semisupervised Learning Framework for Mobile App Classification With the rapid growth of mobile Apps, it is necessary to classify the mobile Apps into predefined categories. However, there are two problems that make this task challenging. First, the name of a mobile App is usually short and ambiguous to reflect its real semantic meaning. Second, it is usually difficult to collect adequate labeled samples to train a good classifier when a customized taxonomy of mobile Apps is required. For the first problem, we leverage Web knowledge to enrich the textual information of mobile Apps. For the second problem, the mostly utilized approach is the semisupervised learning, which exploits unlabeled samples in a cotraining scheme. However, how to enhance the diversity between base learners to maximize the power of the cotraining scheme is still an open problem. Aiming at this problem, we exploit totally different machine learning paradigms (i.e., shallow learning and deep learning) to ensure a greater degree of diversity. To this end, this paper proposes CoDSL, a collaborative deep and shallow semisupervised learning framework, for mobile App classification using only a few labeled samples and a large number of unlabeled samples. The experiment results demonstrate the effectiveness of CoDSL, which could achieve over 85% classification accuracy by using only two labeled samples from each mobile App category.

Discriminative Regularized Deep Generative Models for SemiSupervised Learning Deep generative models (DGMs) have shown strong performance in semisupervised learning (SSL), which incorporate discrete class information into the learning process. Yet existing methods generally overfit to the given labeled data, for only considering the conditional probability of labels. In this paper, we propose a novel discriminative regularized deep generative method for SSL, which fully exploits the discriminative and geometric information of data to address the aforementioned issue. Our method introduces the cluster and manifold assumption that maximizes the classification margin between clusters and simultaneously smooths the predictions of the data which is close in the submanifold of each cluster, to regularize the learning of the classifier in DGMs. To derive the regularization based on introduced assumptions, we adopt the generated data of DGMs along with labelled and unlabelled data, to model the data manifold and yield clusters based on the Gumbelsoftmax distribution. Experimental results on both text and image datasets demonstrate the effectiveness and flexibility of our method, and prove that two introduced assumptions are complementary in guiding the classification boundary, thus improving the discriminative ability of the classifier.