Exploring Bayesian Models for Multi-level Clustering of Hierarchically Grouped Sequential Data
A wide range of Bayesian models have been proposed for data that is divided hierarchically into groups. These models aim to cluster the data at different levels of grouping, by assigning a mixture component to each datapoint, and a mixture distribution to each group. Multi-level clustering is facilitated by the sharing of these components and distributions by the groups. In this paper, we introduce the concept of Degree of Sharing (DoS) for the mixture components and distributions, with an aim to analyze and classify various existing models. Next we introduce a generalized hierarchical Bayesian model, of which the existing models can be shown to be special cases. Unlike most of these models, our model takes into account the sequential nature of the data, and various other temporal structures at different levels while assigning mixture components and distributions. We show one specialization of this model aimed at hierarchical segmentation of news transcripts, and present a Gibbs Sampling based inference algorithm for it. We also show experimentally that the proposed model outperforms existing models for the same task.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringSimilar Papers 제목 키워드 기반
Hierarchically Clustered Representation Learning
The joint optimization of representation learning and clustering in the embedding space has experienced a breakthrough in recent years. In spite of the advance, clustering with representation learning has been limited to…
ClusteringRepresentation LearningReciprocal Multi-Layer Subspace Learning for Multi-View Clustering
Multi-view clustering is a long-standing important research topic, however, remains challenging when handling high-dimensional data and simultaneously exploring the consistency and complementarity of different views. In …
ClusteringHierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference
Multicollinearity is a long lasting challenge in observational causal inference, especially in regressions -- highly correlated independent variables make it hard to isolate their individual impacts on outcomes of intere…
Causal InferenceBayesian Hierarchical Clustering with Exponential Family: Small-Variance Asymptotics and Reducibility
Bayesian hierarchical clustering (BHC) is an agglomerative clustering method, where a probabilistic model is defined and its marginal likelihoods are evaluated to decide which clusters to merge. While BHC provides a few …
ClusteringMultilevel Clustering via Wasserstein Means
We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a potentially large hierarchically structured …
Clustering