Recent domain adaptation methods successfully learn crossdomain transforms to map points between source and target domains. Yet, these methods are either restricted to a single training domain, or assume that the separation into source domains is known a priori. However, most available training data contains multiple unknown domains. In this paper, we present both a novel domain transform mixtu...

The two main goals in model selection are firstly introducing an approach to test homogeneity of several rival models and secondly selecting a set of reasonable models or estimating the best rival model to the true one. In this paper we extend Vuong's method for several models to cluster them. Based on the working paper of Katayama $(2008)$, we propose an approach to test whether rival models h...

Identifying clusters is an important aspect of data analysis. This paper proposes a noveldata clustering algorithm to increase the clustering accuracy. A novel game theoretic self-organizingmap (NGTSOM ) and neural gas (NG) are used in combination with Competitive Hebbian Learning(CHL) to improve the quality of the map and provide a better vector quantization (VQ) for clusteringdata. Different ...

A new simple test to detect within-family clustering of infected individuals is proposed. The test is derived as the score test for several different parametric models designed to allow an increased within-family infectivity. The new test is compared with other tests proposed to detect the same type of clustering caused by increased within-family infectivity. Applications of household disease d...

The fundamental goal of this research has been to improve computational efficiency of the Visually Empirical Region of Influence (VERI) 1 based clustering and pattern recognition (PR) algorithms we developed in previous work. The original clustering algorithm, when applied to data sets with N points, ran in time proportional to N 3 (denoted with the notation O (N 3)), which limited the size of ...