Extreme Semi-supervised Learning for Multiclass Classification

Publication date: Available online 25 September 2019Source: NeurocomputingAuthor(s): Chuangquan Chen, Yanfen Gan, Chi-Man VongAbstractSemi-Supervised Support Vector Machines (S3VMs) provide a powerful framework for Semi-Supervised Learning (SSL) tasks which leverage widely available unlabeled data to improve performance. However, there exists three issues in S3VMs: i) S3VMs require concurrently training c one-against-all (OAA) classifiers (c is the number of classes) for multiclass classification, which is prohibitive for large c; ii) S3VMs require huge computational time and large storage (because of large kernel matrix) in large-scale training and testing; iii) S3VMs require the balance constraint in the unlabeled data, which not only needs prior knowledge from the unlabeled data (the prior knowledge is unavailable in some applications), but also makes their nonconvex optimization problem more intractable. To address these issues, a novel method called Extreme Semi-Supervised Learning (ESSL) is proposed in this paper. First, the framework of Extreme Learning Machine (ELM) is adopted to handle both binary and multiclass classification problems in a unified model. Second, the hidden layer is encoded by an extremely small approximate empirical kernel map (AEKM) to greatly reduce the computational cost and the memory usage for training and testing. Third, the balance constraint (prior knowledge) in the unlabeled data is removed through the elaborative design of weighting functi...
Source: Neurocomputing - Category: Neuroscience Source Type: research