Learning From Label Proportion with Online Pseudo-Label Decision by Regret Minimization
- Resource Type
- Conference
- Authors
- Matsuo, Shinnosuke; Bise, Ryoma; Uchida, Seiichi; Suehiro, Daiki
- Source
- ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on. :1-5 Jun, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Signal processing
Benchmark testing
Minimization
Acoustics
Speech processing
Learning from label proportion
online decision-making
pseudo-labeling
- Language
- ISSN
- 2379-190X
This paper proposes a novel and efficient method for Learning from Label Proportions (LLP), whose goal is to train a classifier only by using the class label proportions of instance sets, called bags. We propose a novel LLP method based on an online pseudo-labeling method with regret minimization. As opposed to the previous LLP methods, the proposed method effectively works even if the bag sizes are large. We demonstrate the effectiveness of the proposed method using some benchmark datasets.