使用受限制的博尔兹曼机器的学习动态进行无监督的等级聚类
Aurélien Decelle1, Beatriz Seoane, Lorenzo Rosset2
1Departamento de Física Teórica, Universidad Complutense de Madrid, 28040 Madrid, Spain and Université Paris-Saclay, CNRS, INRIA Tau team, LISN, 91190 Gif-sur-Yvette, France.
Physical review. E
|August 16, 2023
概括
我们开发了一种使用受限制的博尔茨曼机器的新方法,可以自动发现复杂数据集中隐藏的层次结构. 这种方法有助于理解数据中的关系,例如蛋白质家族.
科学领域:
- 计算生物学是一种计算生物学.
- 机器学习 机器学习
- 数据科学是数据科学.
背景情况:
- 现实世界的数据集往往表现出复杂的层次结构.
- 识别这些隐藏的数据结构对于各种应用程序至关重要.
研究的目的:
- 为构建关系数据树提供一个通用的,可解释的方法.
- 为了利用受限制的博尔兹曼机器的学习动态来进行结构发现.
主要方法:
- 利用了从无序系统中的普莱夫卡扩张得出的平均场方法.
- 将该方法应用于人工生成和现实世界的数据集 (数字图像,基因组突变,蛋白质家族).
主要成果:
- 该方法成功地并自动识别了所有测试数据集中的等级结构.
- 在发现生物数据中的复杂关系方面表现出有效性.
结论:
- 开发的方法为发现层次数据结构提供了一个有效的工具.
- 潜在的应用包括对同源蛋白序列的研究,以获得功能和进化见解.
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