Structured Output-Associative Regression

Structured outputs such as multidimensional vectors or graphs are frequently encountered in real world pattern recognition applications such as computer vision, natural language processing or computational biology. This motivates the learning of functional dependencies between spaces with complex, interdependent inputs and outputs, as arising e.g. from images and their corresponding 3d scene representations. In this spirit, we propose a new structured learning method—Structured Output-Associative Regression (SOAR)—that models not only the input-dependency but also the self-dependency of outputs, in order to provide an output re-correlation mechanism that complements the (more standard) input-based regressive prediction. The model is simple but powerful, and, in principle, applicable in conjunction with any existing regression algorithms. SOAR can be kernelized to deal with non-linear problems and learning is efficient via primal/dual formulations not unlike ones used for kernel ...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Liefeng Bo and Cristian Sminchisescu
• Keywords: Computer Vision, CVPR 2009, Machine Learning And Computer Vision