DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder (2023-05-23T00:00:00.000000Z)

TL;DR

A nonparametric deep clustering framework that employs an infinite mixture of Gaussians as a prior, 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.

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

Authors

Hang Su

6 papers

Alois Knoll

2 papers

Zhenshan Bing

1 papers

TL;DR

Abstract

Authors

References38 items

Contact-Aware Shaping and Maintenance of Deformable Linear Objects With Fixtures

A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions

DeepDPM: Deep Clustering With an Unknown Number of Clusters

K-Means Clustering

Deep Clustering using Dirichlet Process Gaussian Mixture

Improved Baselines with Momentum Contrastive Learning

t-Distributed Stochastic Neighbor Embedding (t-SNE): A tool for eco-physiological transcriptomic analysis.

VSB-DVM: An End-to-End Bayesian Nonparametric Generalization of Deep Variational Mixture Model

A Continual Learning Survey: Defying Forgetting in Classification Tasks

A tutorial on Dirichlet Process mixture modeling.

Gaussian Mixture Models

A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

DeepCluster: A General Clustering Framework Based on Deep Learning

Short text clustering based on Pitman-Yor process mixture model

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Nonparametric Variational Auto-Encoders for Hierarchical Representation Learning

A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering

Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering

Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders

Stick-Breaking Variational Autoencoders

Unsupervised Deep Embedding for Clustering Analysis

Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams

A dirichlet multinomial mixture model-based approach for short text clustering

Auto-Encoding Variational Bayes

Memoized Online Variational Inference for Dirichlet Process Mixture Models

Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation

A Survey of Text Clustering Algorithms

An Analysis of Single-Layer Networks in Unsupervised Feature Learning

Scikit-learn: Machine Learning in Python

ImageNet: A large-scale hierarchical image database

Hierarchical Topic Models and the Nested Chinese Restaurant Process

Catastrophic Forgetting, Rehearsal and Pseudorehearsal

Deep Dirichlet process mixture models

Online Neural Sequence Detection with Hierarchical Dirichlet Point Process

GAN-Based Anomaly Detection In Imbalance Problems

Dirichlet Process

Reading Digits in Natural Images with Unsupervised Feature Learning

Bayesian Nonparametric Models

Field of Study