This paper proposes an image compression approach, in which we incorporate primal sketch based learning into the mainstream image compression framework. The key idea of our approa...
We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a larger set of unlabeled objects and the assumption of a latent tree-structure ...
Charles Kemp, Thomas L. Griffiths, Sean Stromsten,...
KDD is a complex and demanding task. While a large number of methods has been established for numerous problems, many challenges remain to be solved. New tasks emerge requiring th...
Ingo Mierswa, Michael Wurst, Ralf Klinkenberg, Mar...
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Compo...
Exploratory search, in which a user investigates complex concepts, is cumbersome with today’s search engines. We present a new exploratory search approach that generates interac...
Brent Hecht, Samuel Carton, Mahmood Quaderi, Johan...