In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing actions on an object. However, performing many actions on a target object require...

1 Currently on sabbatical leave at the Fraunhofer Institute for Experimental Software Engineering, Sauerwiesen 6, D-67661 Kaiserslautern, Germany. This work has been funded in part under the Austrian Science Fund grant J-1948-INF.

Process families consist of different related variants that represent the same process. This might include, for example, processes executed similarly by different organizations or different versions of a same process with varying features. Motivated by the need to manage variability in process families, recent advances in process mining make it possible to discover, from a collection of event l...

Process models can be seen as “maps” describing the operational processes of organizations. Traditional process discovery algorithms have problems dealing with fine-grained event logs and lessstructured processes. The discovered models (i.e., “maps”) are spaghettilike and are difficult to comprehend or even misleading. One of the reasons for this can be attributed to the fact that the discovere...

The Hierarchical Dirichlet Process (HDP) is a versatile, albeit computationally expensive tool for statistical modeling of mixture models. In this paper, we introduce a spectral algorithm. We show that it is both computationally and statistically efficient. In particular, we derive the lower-order moments of the HDP and give reconstruction guarantees. Moreover, we show that hierarchical spectra...

The hierarchical Dirichlet process (HDP) can provide a nonparametric prior for a mixture model with grouped data, where mixture components are shared across groups. However, the computational cost is generally very high in terms of both time and space complexity. Therefore, developing a method for fast inference of HDP remains a challenge. In this paper, we assume a symmetric multiprocessing (S...

The task of predicting retweet behavior is an important and essential step for various social network applications, such as business intelligence, popular event prediction, and so on. Due to the increasing requirements, in recent years, the task has attracted extensive attentions. In this work, we propose a novel method using non-parametric statistical models to combine structural, textual, and...

As a result of their good performance in practice and their desirable analytical properties, Gaussian process regression models are becoming increasingly of interest in statistics, engineering and other fields. However, two major problems arise when the model is applied to a large data-set with repeated measurements. One stems from the systematic heterogeneity among the different replications, ...

Research on inductive process modeling combines background knowledge with time-series data to construct explanatory models, but previous work has placed few constraints on search through the model space. We present an extended formalism that organizes process knowledge in a hierarchical manner, and we describe HIPM, a system that carries out constrained search for hierarchical process models. W...