一个半监督的堆叠自动编码器使用伪标签进行分类任务
Jie Lai1, Xiaodan Wang1, Qian Xiang1
1College of Air and Missile Defense, Air Force Engineering University, Xi'an 710051, China.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
这项研究引入了一种基于伪标签的新型半监督堆叠自动编码器 (PL-SSAE),以有效地利用标记和未标记的数据来改进分类任务. 通过伪标签,PL-SSAE通过利用未标记的样本来提高性能.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 手动标记样本是低效的,导致许多未标记的训练样本在实际场景.
- 半监督学习旨在利用标记和未标记的数据,这对于克服标记限制至关重要.
- 传统的堆叠自动编码器 (SAE) 受到监督,并且由于仅依赖标记数据,因此在半监督任务中扎.
研究的目的:
- 为有效的半监督分类提出基于伪标签的新型半监督堆叠自编码器 (PL-SSAE).
- 在堆叠的自动编码器模型中增强未标记数据的利用.
- 在有限的标记数据的情况下改善分类性能.
主要方法:
- 在堆叠的自编码器框架中引入了一个伪标签方法,创建了PL-SSAE.
- 在所有可用样本上使用自动编码器 (AE) 来初始化网络参数的未经监督的雇员预培训.
- 使用标记样本实现代微调,为未标记样本生成伪标签,然后用于规范化.
主要成果:
- 通过在微调过程中将伪标签数据纳入PL-SSAE,有效地利用了标记和未标记的样本.
- 经验评估表明,PL-SSAE在基准数据集上显著优于传统的SAE,SSAE,半SAE和半SSAE.
- 拟议的方法显示了具有竞争力的半监督分类性能.
结论:
- 通过有效利用未标记的样本,PL-SSAE成功地解决了有限的标记数据的挑战.
- 伪标签的集成增强了堆叠的自动编码器在半监督学习任务的能力.
- 与现有的方法相比,PL-SSAE为半监督分类提供了更具竞争力和有效的方法.
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