Multi-Output Regularized Projection

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 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.

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Kai Yu, Shipeng Yu, Volker Tresp
• Keywords: Computer Vision, CVPR 2005, Dimensionality Reduction, Feature Projection, Features Prediction Accuracy, Structured Outputs, Training Data Set