基于热图的后部规则化
Maxwell W Libbrecht1, Michael M Hoffman2, Jeffrey A Bilmes3
1Genome Sciences, Box 355065, Foege Building, S220B, 3720 15th Ave NE, Seattle, WA 98195-5065.
概括
这项研究为无监督生成模型引入了基于图的新型后部调节器,增强了附近变量的后部分布相似性. 该方法在计算生物学应用中提高了性能,例如基因组数据分析.
科学领域:
- 机器学习 机器学习
- 计算生物学 计算生物学
背景情况:
- 图形流性目标在半监督学习中是成功的,但在无监督生成模型中未得到充分利用.
- 概率模型往往缺乏机制来强制执行相关变量的后置分布之间的相似性.
研究的目的:
- 为无监督生成模型引入一种新的类型的基于图的后部调节器.
- 为这些调节器开发一个高效的推理和参数学习算法.
- 将该方法应用于计算生物学,特别是基因组数据分析.
主要方法:
- 定义基于图的后部调节器,以鼓励附近变量的类似后部分布.
- 开发了一种三向交替优化算法,用于推理和参数学习的闭式更新.
- 算法更新在图形度上是线性的,表现出单调的收,并且可以并行.
主要成果:
- 拟议的方法在合成问题上优于现有的基于图形的规范化技术.
- 它还超越了使用现有的近似推断方法进行长距离相互作用的可比策略.
- 在整合3D基因组相互作用数据时,在预测基因组活动方面观察到显著的改进.
结论:
- 新型规范化器有效地增强了无监督学习的概率模型.
- 有效的优化算法可方便实际应用.
- 该方法在计算生物学中对基因组数据注释和预测具有实质性的实用性.
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