—In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting ...
High-dimensional data usually incur learning deficiencies and computational difficulties. We present a novel semi-supervised dimensionality reduction technique that embeds high-dim...
Many classes of image data span a low dimensional nonlinear space embedded in the natural high dimensional image space. We adopt and generalize a recently proposed dimensionality ...
The dimensionality curse has profound e ects on the effectiveness of high-dimensional similarity indexing from the performance perspective. One of the well known techniques for im...
We introduce a variational inference framework for training the Gaussian process latent variable model and thus performing Bayesian nonlinear dimensionality reduction. This method...