用自编码器与UMAP结合提升聚类效果，无需标签也能更好分组数据。
Autoencoded UMAP-Enhanced Clustering for Unsupervised Learning
- 先用自编码器压缩数据，再用UMAP做非线性降维。
- 在MNIST上聚类准确率超过现有方法，性能显著提升。
- 适合想提升无监督聚类效果的研究者或工程师。
我们提出一种新的无监督学习方法：将数据非线性嵌入低维空间，再使用传统聚类算法进行聚类。该嵌入由自编码器的编码器和UMAP算法的输出共同构成。自编码器采用复合损失函数训练，包含数据重建项作为正则化，以及基于谱图理论构建的促进聚类项。两个嵌入结果与后续聚类被整合为三阶段无监督学习框架，称为自编码增强型UMAP聚类（AUEC）。在MNIST数据集上，AUEC的聚类准确率显著优于当前最优技术。
原文摘要 · Abstract (English)
We propose a novel approach to unsupervised learning by constructing a non-linear embedding of the data into a low-dimensional space followed by any conventional clustering algorithm. The embedding promotes clusterability of the data and is comprised of two mappings: the encoder of an autoencoder neural network and the output of UMAP algorithm. The autoencoder is trained with a composite loss function that incorporates both a conventional data reconstruction as a regularization component and a clustering-promoting component built using the spectral graph theory. The two embeddings and the subsequent clustering are integrated into a three-stage unsupervised learning framework, referred to as Autoencoded UMAP-Enhanced Clustering (AUEC). When applied to MNIST data, AUEC significantly outperforms the state-of-the-art techniques in terms of clustering accuracy.
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