This paper aims to study the deep clustering problem with heterogeneous features and unknown cluster number. To address this issue, a novel deep Bayesian clustering framework is proposed. In particular, a heterogeneous feature metric is first ...

Bayesian estimation of parameters in the Dirichlet mixture process of the Beta‐Liouville distribution (i.e., the infinite Beta‐Liouville mixture model) has recently gained considerable attention due to its modeling capability for proportional ...

Bayesian hierarchical models are powerful tools for learning common latent features across multiple data sources. The Hierarchical Dirichlet Process (HDP) is invoked when the number of latent components is a priori unknown. While there is a rich ...

Although multivariate count data are routinely collected in many application areas, there is surprisingly little work developing flexible models for characterizing their dependence structure. This is particularly true when interest focuses on inferring ...

Being a widely studied model in machine learning and multimedia community, Deep Neural Network (DNN) has achieved an encouraging success in various applications. However, conventional DNN suffers the difficulty when handling the open set learning ...

Climate prediction is a very challenging problem. Many institutes around the world try to predict climate variables by building climate models called General Circulation Models (GCMs), which are based on mathematical equations that describe the physical ...

Short text stream clustering has become an increasingly important problem due to the explosive growth of short text in diverse social medias. In this paper, we propose a model-based short text stream clustering algorithm (MStream) which can deal with ...

The availability of geographical and temporal tagged documents enables many location and time based mining tasks. Event discovery is one of such tasks, which is to identify interesting happenings in the geographical and temporal space. In recent years, ...

Water pipe failures can cause significant economic and social costs, hence have become the primary challenge to water utilities. In this paper, we propose a Bayesian nonparametric approach, namely the Dirichlet process mixture of hierarchical beta ...

Clusters in document streams, such as online news articles, can be induced by their textual contents, as well as by the temporal dynamics of their arriving patterns. Can we leverage both sources of information to obtain a better clustering of the ...

Automatic software categorization is the task of assigning software systems or libraries to categories based on their functionality. Correctly assigning these categories is essential to ensure that relevant software can be easily retrieved by developers ...

In this paper, we preliminarily learn the problem of reconstructing users' life history based on the their Twitter stream and proposed an unsupervised framework that create a chronological list for personal important events (PIE) of individuals. By ...

Nowadays, large collections of photos are tagged with GPS coordinates. The modelling of such large geo-tagged corpora is an important problem in data mining and information retrieval, and involves the use of geographical information to detect topics with ...

Discrete mixtures are used routinely in broad sweeping applications ranging from unsupervised settings to fully supervised multi-task learning. Indeed, finite mixtures and infinite mixtures, relying on Dirichlet processes and modifications, have become ...

In this paper, we propose a novel Bayesian nonparametric statistical approach of simultaneous clustering and localized feature selection for unsupervised learning. The proposed model is based on a mixture of Dirichlet processes with generalized ...

Subjective logic is a powerful probabilistic logic which is useful to handle data in case of uncertainty. Subjective logic and the Semantic Web can mutually benefit from each other, since subjective logic is useful to handle the inner noisiness of the ...

Community and topic are two widely studied patterns in social network analysis. However, most existing studies either utilize textual content to improve the community detection or use link structure to guide topic modeling. Recently, some studies take ...

Visual summarization of landmarks is an interesting and non-trivial task with the availability of gigantic community-contributed resources. In this work, we investigate ways to generate representative and distinctive views of landmarks by automatically ...

Nonparametric Bayesian methods are employed to constitute a mixture of low-rank Gaussians, for data x ∈ R^N that are of high dimension N but are constrained to reside in a low-dimensional subregion of R^N. The number of mixture components and their rank ...

Statistical relational learning (SRL) provides effective techniques to analyze social network data with rich collections of objects and complex networks. Infinite hidden relational models (IHRMs) introduce nonparametric mixture models into relational ...