A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental semi-supervised subspace learning for image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
New approaches to support vector ordinal regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Prediction of Ordinal Classes Using Regression Trees
Fundamenta Informaticae - Intelligent Systems
Spectral regression: a unified subspace learning framework for content-based image retrieval
Proceedings of the 15th international conference on Multimedia
Kernel Discriminant Learning for Ordinal Regression
IEEE Transactions on Knowledge and Data Engineering
Semi-supervised manifold ordinal regression for image ranking
MM '11 Proceedings of the 19th ACM international conference on Multimedia
| Hi-index | 0.00 |
Ordinal regression, which aims at determining the rating of a data item on a discrete rating scale, is an important research topic in pattern mining and multimedia data analysis. Most of the existing approaches of ordinal regression try to seek only one direction on which the projected data are well ranked. This setting largely limits the discriminative ability and may not describe the complicated distribution of the dataset very well. In this paper, we proposed a new algorithm called Neighborhood Preserving Ordinal Regression (NPOR), which aims to extract multiple projection directions from the original dataset according to the maximum margin and manifold preserving criteria. By optimizing the order information of the observations and preserving the intrinsic geometry of the dataset in a unified framework, NPOR provides faithful ordinal regression results to the new coming data samples. Experiments on various data sets demonstrate the effectiveness of the proposed algorithm.