An unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of i...
The Dirichlet Process Mixture (DPM) models represent an attractive approach to modeling latent distributions parametrically. In DPM models the Dirichlet process (DP) is applied es...
Asma Rabaoui, Nicolas Viandier, Juliette Marais, E...
We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...
Recent advances in genotyping technology have led to the generation of an enormous quantity of genetic data. Traditional methods of statistical analysis have proved insufficient i...
Emily Rose Holzinger, Carrie C. Buchanan, Scott M....
Experimental and computational studies suggest that complex motor behavior is based on simpler spatiotemporal primitives, or synergies. This has been demonstrated by application o...