Interactive Generative Manifold Learning

Exploratory data analysis is the foremost step in selecting appropriate statistical learning algorithms specialized to a dataset. We have proposed a generative framework for visualizing high dimensional data as low-dimensional manifold embedded in a high dimensional space. The method allows user to conveniently explore the space using fewer dimensions while still capturing the principal modes of variations of the high dimensional data. Specifically, we employ Gaussian Process Latent Variable Model (GPLVM) and Spectral Latent Variable Model (SLVM) to learn low-dimensional representations of the data. Probabilistic mappings between the embeddings and the original space facilitate efficient interpolation in the latent space as well as fast visualization of the interpolated latent points in the original space. To allow the user to span the manifold in an intuitive manner, we develop supervised and semi-supervised tools to relate the latent space to the meaningful feature space. These enable computation of principle direction for each point in the latent space to allow the user to traverse in a meaningful way. Further, we have proposed a principled approach to extrapolate the latent space by predicting the manifold structure in regions lying outside the existing domain of the data.