DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder

TL;DR

DIVA introduces a nonparametric deep clustering method using Dirichlet processes and variational auto-encoders, enabling dynamic, incremental clustering without prior knowledge of the number of clusters, and outperforms existing methods.

Contribution

It presents a novel nonparametric deep clustering framework that adaptively handles dynamic features using an infinite mixture model and online variational inference.

Findings

Outperforms state-of-the-art clustering baselines.

Effectively handles incremental and dynamic features.

Demonstrates superior classification accuracy on complex data.

Abstract

Generative model-based deep clustering frameworks excel in classifying complex data, but are limited in handling dynamic and complex features because they require prior knowledge of the number of clusters. In this paper, we propose a nonparametric deep clustering framework that employs an infinite mixture of Gaussians as a prior. Our framework utilizes a memoized online variational inference method that enables the "birth" and "merge" moves of clusters, allowing our framework to cluster data in a "dynamic-adaptive" manner, without requiring prior knowledge of the number of features. We name the framework as DIVA, a Dirichlet Process-based Incremental deep clustering framework via Variational Auto-Encoder. Our framework, which outperforms state-of-the-art baselines, exhibits superior performance in classifying complex data with dynamically changing features, particularly in the case of incremental features. We released our source code implementation at: https://github.com/Ghiara/diva