Many clustering algorithms only find one clustering solution. However, data can often be grouped and interpreted in many different ways. This is particularly true in the high-dim...
Semantically heterogeneous and distributed data sources are quite common in several application domains such as bioinformatics and security informatics. In such a setting, each dat...
While empirical evaluations are a common research method in some areas of Artificial Intelligence (AI), others still neglect this approach. This article outlines both the opportun...
In this paper, we study the problem of learning in the presence of classification noise in the probabilistic learning model of Valiant and its variants. In order to identify the cl...
Discrete mixed membership modeling and continuous latent factor modeling (also known as matrix factorization) are two popular, complementary approaches to dyadic data analysis. In...