A new method for DPMM inference without truncation.
problem Inference in DPMM requires truncation, limiting model flexibility.
method Hybrid inference combining MCMC and variational methods.
result Efficient variational updates without increased model complexity.
Adapts DPMM for fast streaming data clustering.
problem Clustering streaming data with time-dependent statistics.
method Adapts DPMM and sampling-based inference for online clustering.
result Obtains state-of-the-art results in speed and accuracy.
New software package for scalable DPMM inference on large datasets.
problem Scalability and practical adoption of Dirichlet Process Mixture Models.
method Efficient distributed sampling-based inference on CPUs and GPUs.
result Significant speedups and fitting of larger datasets.
Study on posterior distribution for cluster number in DPMM models.
problem Understanding the posterior distribution of the number of clusters in Dirichlet process mixture models.
method Rigorous study of posterior distribution under different prior distributions and constraints on data distributions.
result Provide novel lower bounds on the ratios of probabilities between s+1 clusters and s clusters. A new model aligns sequences using DPMM, outperforming GP-LVM.
problem Aligning high-dimensional time-warped sequences without supervision.
method Dirichlet Process Mixture Model (DPMM) with Gaussian Processes (GPs).
result DPMM achieves competitive results compared to GP-LVM on synthetic and real-world data.
A new parallel clustering method improves speed and accuracy for single cell transcriptomic data.
problem Challenges in clustering single cell transcriptomic data, including poor quality, lack of prior knowledge, and slow computation.
method Parallel Split Merge Sampling on Dirichlet Process Mixture Model (Para-DPMM).
result The Para-DPMM model outperforms existing methods in clustering quality and computational speed.
DPMM-CFL clusters clients for federated learning without fixed K, improving performance.
problem Improving federated learning performance under non-IID client heterogeneity.
method DPMM-CFL uses a Dirichlet Process Mixture Model to infer both cluster number and client assignments.
result DPMM-CFL optimizes per-cluster federated objectives and jointly infers cluster number and assignments.
New method for scalable DPMM estimation in distributed data.
problem Efficiently handling new components in distributed Dirichlet Process Mixture Models.
method Locally creating new components, probabilistically consolidating them, and maintaining consistency with low communication cost.
result High scalability and consistent estimation in distributed and asynchronous environments.
Novel connections between Neyman-Scott processes and Bayesian nonparametric mixture models enable scalable inference.
problem Efficiently modeling and detecting clusters in spatiotemporal data.
method Adapting collapsed Gibbs sampling for Neyman-Scott processes via connections to mixture of finite mixture models.
result Demonstrated scalability and effectiveness on neural spike trains and document streams.
A new method speeds up DPMM inference for federated learning.
problem Slow inference for large datasets in DPMMs.
method Distributed collapsed Gibbs sampler (DisCGS) for DPMMs.
result Significant reduction in execution time (200x faster) for large datasets.
DPGIIL clusters structural anomalies using transmissibility functions with deep learning and Dirichlet process.
problem Clustering structural anomalies in high-dimensional streaming data with optimal cluster number determination.
method Combines Dirichlet process and deep generative models for incremental learning and anomaly detection.
result DPGIIL outperforms traditional methods in anomaly detection and clustering.
Adaptive scan Gibbs sampler improves large-scale inference performance.
problem Efficiently updating large-scale online inference problems.
method Derives an adaptive scan Gibbs sampler that optimizes mini-batch size selection.
result Demonstrates superior performance compared to collapsed Gibbs sampler.
The paper proposes a method to model non-smooth functions using clustering, classification, and Gaussian process modeling.
problem Modeling discontinuities and non-smoothness in expensive computational models.
method Three-stage approach combining clustering, classification, and Gaussian process modeling.
result The approach successfully models discontinuities and non-smoothness in various functions.
Proposes a new model for clustering passenger trips considering hierarchical and multi-dimensional data.
problem Clustering passenger trips with hierarchical and multi-dimensional data, especially in large-scale transportation systems.
method Tensor Dirichlet Process Multinomial Mixture (Tensor-DPMM) model, incorporating Dirichlet Process for automatic cluster number determination and tensor representation for multi-mode data.
result Automatic determination of the number of clusters and improved clustering quality.
The parsimonious Gaussian mixture models, which exploit an eigenvalue decomposition of the group covariance matrices of the Gaussian mixture, have shown their success in particular in cluster analysis. Their estimation is in general performed by maximum likelihood estimation and has also been considered from a parametr…