Deep clustering analysis via variational autoencoder with Gamma mixture latent embeddings.

Area of Science:

• Machine Learning
• Deep Learning
• Unsupervised Learning

Background:

• Variational Autoencoders (VAEs) are commonly used for deep clustering.
• Gaussian Mixture Models (GMMs) are often employed as priors in VAE-based clustering.
• Existing methods may have limitations in capturing complex latent space structures.

Purpose of the Study:

• To propose a novel deep clustering model, GamMM-VAE, for unsupervised learning.
• To enhance latent space representation quality using a flexible asymmetric Gamma mixture model.
• To enable effective gradient backpropagation through a novel Gamma distribution reparameterization trick.

Main Methods:

• Developed GamMM-VAE, a deep clustering model integrating VAE with an asymmetric Gamma mixture model.
• Introduced a transformation method from Gaussian to Gamma distribution to facilitate VAE's reparameterization trick.
• Derived the evidence lower bound (ELBO) using the Gamma mixture model for optimization via stochastic gradient variational Bayesian (SGVB) estimation.

Main Results:

• GamMM-VAE demonstrated superior performance compared to state-of-the-art deep clustering models across six benchmark datasets.
• The model successfully learned high-quality latent representations for effective clustering.
• The generative capabilities of GamMM-VAE allowed for the creation of realistic class-specific samples without supervision.

Conclusions:

• The proposed GamMM-VAE offers an effective approach to deep clustering with improved latent space modeling.
• The novel Gamma distribution reparameterization trick enhances the applicability of VAEs in clustering tasks.
• GamMM-VAE shows promise for both clustering and data generation in unsupervised learning scenarios.