In multilabel classification, strong label dependence is present for exploiting, particularly for word-to-word dependence defined by semantic labels. In such a situation, we develop a collaborative-learning framework to predict class labels based on label-predictor pairs and label-only data. For example, in image categorization and recognition, language expressions describe the content of an image together with a large number of words and phrases without associated images. This article proposes a new loss quantifying partial correctness for false positive and negative misclassifications due to label similarities. Given this loss, we develop the Bayes rule to capture label dependence by nonlinear classification. On this ground, we introduce a weighted random forest classifier for complete data and a stacking scheme for leveraging additional labels to enhance the performance of supervised learning based on label-predictor pairs. Importantly, we decompose multilabel classification into a sequence of independent learning tasks, based on which the computational complexity of our classifier becomes linear in the size of labels. Compared to existing classifiers without label-only data, the proposed classifier enjoys the computational benefit while enabling the detection of novel labels absent from training by exploring label dependence and leveraging label-only data for higher accuracy. Theoretically, we show that the proposed method reconstructs the Bayes performance consistently, achieving the desired learning accuracy. Numerically, we demonstrate that the proposed method compares favorably in terms of the proposed and Hamming losses against binary relevance and a regularized Ising classifier modeling conditional label dependence. Indeed, leveraging additional labels tends to improve the supervised performance, especially when the training sample is not very large, as in semisupervised learning. Finally, we demonstrate the utility of the proposed approach on the Microsoft COCO object detection challenge, PASCAL visual object classes challenge 2007, and Mediamill benchmark.
Bibliographical notePublisher Copyright:
© 2021 American Statistical Association.
- Binary relevance
- Label dependence
- Nonconvex minimization
- Novel labels