Deep Generative Clustering with VAEs and Expectation-Maximization
This addresses clustering challenges in machine learning by providing a more effective method without needing Gaussian Mixture Model priors or extra regularization.
The paper tackled the problem of deep clustering by integrating Variational Autoencoders into the Expectation-Maximization framework, achieving superior clustering performance on MNIST and FashionMNIST datasets compared to state-of-the-art methods.
We propose a novel deep clustering method that integrates Variational Autoencoders (VAEs) into the Expectation-Maximization (EM) framework. Our approach models the probability distribution of each cluster with a VAE and alternates between updating model parameters by maximizing the Evidence Lower Bound (ELBO) of the log-likelihood and refining cluster assignments based on the learned distributions. This enables effective clustering and generation of new samples from each cluster. Unlike existing VAE-based methods, our approach eliminates the need for a Gaussian Mixture Model (GMM) prior or additional regularization techniques. Experiments on MNIST and FashionMNIST demonstrate superior clustering performance compared to state-of-the-art methods.