将生成模型与谱聚类结合，实现可解释的聚类结果
Generative Kernel Spectral Clustering
- 用生成模型增强谱聚类，提升可解释性
- 在MNIST和FashionMNIST上学习到有意义的聚类表示
- 适合需要可视化聚类特征的研究者
现代聚类方法常以牺牲可解释性换取性能，尤其在基于深度学习的方法中。我们提出生成核谱聚类（GenKSC），将核谱聚类与生成建模相结合，既能生成清晰的聚类结构，又能提供可解释的表示。通过在加权方差最大化基础上引入重构损失和聚类损失，模型构建了一个可探索的潜在空间，能够沿聚类方向进行可视化遍历，揭示聚类特征。在MNIST和FashionMNIST数据集上的实验表明，该模型能有效学习具有意义的聚类表示。
原文摘要 · Abstract (English)
Modern clustering approaches often trade interpretability for performance, particularly in deep learning-based methods. We present Generative Kernel Spectral Clustering (GenKSC), a novel model combining kernel spectral clustering with generative modeling to produce both well-defined clusters and interpretable representations. By augmenting weighted variance maximization with reconstruction and clustering losses, our model creates an explorable latent space where cluster characteristics can be visualized through traversals along cluster directions. Results on MNIST and FashionMNIST datasets demonstrate the model's ability to learn meaningful cluster representations.
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