We introduce a variational inference framework for training the Gaussian process latent variable model and thus performing Bayesian nonlinear dimensionality reduction. This method...
We consider the problem of approximating a set P of n points in Rd by a j-dimensional subspace under the p measure, in which we wish to minimize the sum of p distances from each p...
Dan Feldman, Morteza Monemizadeh, Christian Sohler...
Energy-minimizing active contour models (snakes) have been proposed for solving many computer vision problems such as object segmentation, surface reconstruction, and object track...
Non-linear dimensionality reduction of noisy data is a challenging problem encountered in a variety of data analysis applications. Recent results in the literature show that spect...
Multiple view data, which have multiple representations from different feature spaces or graph spaces, arise in various data mining applications such as information retrieval, bio...