用相似网络学习鸟类鸣叫的演化树，无需预设声学特征。
Siamese networks for Poincaré embeddings and the reconstruction of evolutionary trees
- 用孪生网络从叶节点样本中学习庞加莱嵌入
- 在六种雀鸟数据上重建出准确的演化树
- 适合研究表型演化与无监督树结构推断
我们提出一种从高维数据重构演化树的方法，以雀鸟鸣叫声谱图为具体应用。针对无法预先定义声学特征的情况下，从表型特征（如鸣叫）推断系统发育关系的挑战，结合庞加莱嵌入进行降维与距离计算，以及邻接法进行树结构重建。不同于以往方法，本工作采用孪生网络仅从潜在树的叶节点样本中学习嵌入表示。我们在合成数据和六种雀鸟的声谱图数据上验证了该方法的有效性。
原文摘要 · Abstract (English)
We present a method for reconstructing evolutionary trees from high-dimensional data, with a specific application to bird song spectrograms. We address the challenge of inferring phylogenetic relationships from phenotypic traits, like vocalizations, without predefined acoustic properties. Our approach combines two main components: Poincaré embeddings for dimensionality reduction and distance computation, and the neighbor joining algorithm for tree reconstruction. Unlike previous work, we employ Siamese networks to learn embeddings from only leaf node samples of the latent tree. We demonstrate our method's effectiveness on both synthetic data and spectrograms from six species of finches.
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