Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 31, Issue 7, Pages 2430-2440Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2019.2929071
Keywords
Labeling; Cognition; Data models; Feature extraction; Deep learning; Learning systems; Emotion recognition; Deep learning; emotion detection; label-less learning; multimodal emotion cognition
Categories
Funding
- National Key Research and Development Program of China [2017YFE0123600]
- National Natural Science Foundation of China [61 802 138]
- China Postdoctoral Science Foundation [2018M632859, 2019T120657]
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In this paper, we propose a label-less learning for emotion cognition (LLEC) to achieve the utilization of a large amount of unlabeled data. We first inspect the unlabeled data from two perspectives, i.e., the feature layer and the decision layer. By utilizing the similarity model and the entropy model, this paper presents a hybrid label-less learning that can automatically label data without human intervention. Then, we design an enhanced hybrid label-less learning to purify the automatic labeled data. To further improve the accuracy of emotion detection model and increase the utilization of unlabeled data, we apply enhanced hybrid label-less learning for multimodal unlabeled emotion data. Finally, we build a real-world test bed to evaluate the LLEC algorithm. The experimental results show that the LLEC algorithm can improve the accuracy of emotion detection significantly.
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