We present a manifold learning approach to dimensionality
reduction that explicitly models the manifold as a mapping
from low to high dimensional space. The manifold is
represen...
In this paper we consider a novel Bayesian interpretation of Fisher's discriminant analysis. We relate Rayleigh's coefficient to a noise model that minimises a cost base...
In this paper we analyze the problem of estimating a function from different noisy data sets collected by spatially distributed sensors and subject to unknown temporal shifts. We p...
One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. D...
Achieving intuitive control of animated surface deformation while observing a specific style is an important but challenging task in computer graphics. Solutions to this task can ...