Generative Kernel Spectral Clustering
This work addresses the need for interpretable clustering in machine learning, though it appears incremental as it builds on existing kernel and generative methods.
The paper tackled the problem of clustering methods trading interpretability for performance by introducing Generative Kernel Spectral Clustering (GenKSC), which combines kernel spectral clustering with generative modeling to produce well-defined clusters and interpretable representations, as demonstrated on MNIST and FashionMNIST datasets.
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.