Probabilistic topic models have become popular as methods for dimensionality reduction in collections of text documents or images. These models are usually treated as generative m...
Dimensionality reduction is an important preprocessing step in high-dimensional data analysis without losing intrinsic information. The problem of semi-supervised nonlinear dimensi...
Image auto-annotation is a challenging task in computer vision. The goal of this task is to predict multiple words for generic images automatically. Recent state-of-theart methods...
Many problems in information processing involve some form of dimensionality reduction. In this paper, we introduce Locality Preserving Projections (LPP). These are linear projecti...
Many applications require analyzing vast amounts of textual data, but the size and inherent noise of such data can make processing very challenging. One approach to these issues i...
David G. Underhill, Luke McDowell, David J. Marche...