Dimensionality reduction has been one of the most active research areas of machine learning in the past few years. Novel algorithms for nonlinear dimensionality reduction (Isomap, locally linear embedding, local tangent space alignment, etc.) and supervised dimensionality reduction (neighborhood components analysis, max-margin matrix factorization, support vector decomposition, etc.) have taken significant steps toward overcoming deficiencies in traditional methods like PCA and Fisher's LDA. It is intuitive that dimensionality reduction helps to visualize data, reduce computational complexity, and avoid overfitting. But in practice there are still relatively few applications which make use of new dimensionality reduction techniques. The goal of this workshop is to understand how to match the capabilities of new nonlinear and supervised dimensionality reduction techniques with practical applications in science, engineering, and technology. We hope to achieve this by bringing together researchers who develop these techniques and those who apply them. A successful workshop will lead to new directions for application-oriented dimensionality reduction research and ignite cross-fertilization between different application domains. We are especially interested in applications from biology (particularly neuroscience and genetics), psychology, human and computer vision, auditory signal processing, and text analysis.