Weakly supervised classification through manifold learning and rank-based contextual measures

Over the last decade, significant advances have been achieved by machine learning approaches, notably in supervised learning scenarios. Supported by the advent of deep learning and comprehensive training sets, the accuracy achieved on classification tasks has improved significantly. Simultaneously, we have experienced massive growth in multimedia data and applications, which have become ubiquitous in several domains. However, with the increase in multimedia data collections, significant bottlenecks associated with the lack of labeled data emerged. To surpass this critical issue, developing methods capable of exploiting the unlabeled data and operating under weak supervision has become imperative. This work proposes a rank-based model capable of using contextual information encoded in the unlabeled data to perform weakly supervised classification. We evaluated the proposed weakly supervised approach on multimedia classification tasks with and without manifold learning algorithms, considering several combinations of rank correlation measures and classifiers. An experimental evaluation was conducted on 6 public image datasets considering different features, including convolutional neural networks and visual transformers. Positive gains were achieved compared to supervised and semi-supervised baselines for the same amount of labeled data. For instance, the proposed approach with manifold learning enhanced the accuracy of the Optimum-Path Forest (OPF) classifier from 71.77% to 83.24% when applied to the Flowers dataset and ResNet features. Among the conclusions, this work reveals that rank-based correlation measures and manifold learning can be used for a more effective labeled set expansion.