来自 | 机器之心
流形学习，自 2000 年在著名的科学杂志《Science》被首次提出以来，已成为信息科学领域的研究热点。可能很多人会问，流形学习有什么用呢？首先流形学习可以作为一种数据降维的方式，第二，流形能够刻画数据的本质。其主要代表方法有等距映射、局部线性嵌入等。那么，具有流形学习 2.0 之称的潜图学习方法如何呢？
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第一是少样本学习：利用基于图的方法从少量样本中进行归纳（重点：只需要少量带有标注的样本）。在计算机视觉中，数据标注量从几千到上万不等，成本很高，因此少样本学习变得越来越重要。
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第二是生物学领域：人们经常通过实验观察生物分子如蛋白质的表达水平，并试图重建它们的相互作用和信号网络。
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第三是对物理系统的分析：其中图可以描述多个对象之间的交互作用。尤其是处理复杂粒子相互作用的物理学家，最近对基于图的方法表现出了浓厚的兴趣。
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第四是 NLP 问题：在 NLP 领域中，图神经网络可以看作是 transformer 架构的泛化。所提到的许多问题也提出了在图结构中加入先验知识，这一结构在很大程度上仍然是开放的：例如，人们可能希望强迫图遵守某些构造规则或与某些统计模型兼容。
参考链接：
https://towardsdatascience.com/manifold-learning-2-99a25eeb677d
https://zhuanlan.zhihu.com/p/75307407?from_voters_page=true
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