Multi-Output Regularized Projection

Kai Yu, Shipeng Yu, V. Tresp
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)  
Dimensionality reduction via feature projection has been widely used in pattern recognition and machine learning. It is often beneficial to derive the projections not only based on the inputs but also on the target values in the training data set. This is of particular importance in predicting multivariate or structured outputs which is an area of growing interest. In this paper we introduce a novel projection framework which is sensitive to both input features and outputs. Based on the derived
more » ... ased on the derived features prediction accuracy can be greatly improved. We validate our approach in two applications. The first is to model users' preferences on a set of paintings. The second application is concerned with image categorization where each image may belong to multiple categories. The proposed algorithm produces very encouraging results in both settings.
doi:10.1109/cvpr.2005.236 dblp:conf/cvpr/YuYT05 fatcat:ufiwl3wzjvdatatxfsqn3e5naa