In this paper, we introduce a generic framework for semi-supervised kernel learning. Given pairwise (dis-)similarity constraints, we learn a kernel matrix over the data that respe...
The Gaussian process latent variable model (GP-LVM) is a generative approach to nonlinear low dimensional embedding, that provides a smooth probabilistic mapping from latent to da...
Linear discriminant analysis (LDA) has been an active topic of research during the last century. However, the existing algorithms have several limitations when applied to visual d...
Abstract—In classical image classification approaches, lowlevel features have been used. But the high dimensionality of feature spaces poses a challenge in terms of feature selec...
Rajeev Agrawal, Changhua Wu, William I. Grosky, Fa...
Abstract— The problem of an effective coordination of multiple autonomous robots is one of the most important tasks of the modern robotics. In turn, it is well known that the lea...