In recent years, data dimensionality has increasingly become a concern, leading to many parameter and dimension reduction techniques being proposed in the literature. A parameter-wise co-clustering model, for (possibly high-dimensional) modelled via continuous random variables, is presented. The although allowing more flexibility, still maintains very high degree of parsimony interpretability a...

Though subspace clustering, ensemble clustering, alternative clustering, and multiview clustering are different approaches motivated by different problems and aiming at different goals, there are similar problems in these fields. Here we shortly survey these areas from the point of view of subspace clustering. Based on this survey, we try to identify problems where the different research areas ...

One of the most serious difficulties in the analysis of high-dimensional data sets involves the treatment of measures of similarity. Although similarity measures often retain some discriminative ability as the dimension increases, the similarity values themselves are often difficult to interpret. Methods for search, clustering and feature selection that perform quantitive tests of similarity va...

We present a new method for discrimination of data classes or data sets in a high-dimensional space. Our approach combines two important relatively new concepts in high-dimensional data analysis, i.e., Diffusion Maps and Earth Mover’s Distance, in a novel manner so that it is more tolerant to noise and honors the characteristic geometry of the data. We also illustrate that this method can be us...