Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of feature subspaces for content-based retrieval using relevance feedback
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Modern Information Retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
(2D)2LDA: An efficient approach for face recognition
Pattern Recognition
Typicality ranking via semi-supervised multiple-instance learning
Proceedings of the 15th international conference on Multimedia
Query dependent ranking using K-nearest neighbor
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Document selection methodologies for efficient and effective learning-to-rank
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning to rank for content-based image retrieval
Proceedings of the international conference on Multimedia information retrieval
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
Supervised reranking for web image search
Proceedings of the international conference on Multimedia
Analysis of facial features in identical twins
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
Ordinal regularized manifold feature extraction for image ranking
Signal Processing
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Learning to rank has been demonstrated as a powerful tool for image ranking, but the issue of the "curse of dimensionality" is a key challenge of learning a ranking model from a large image database. This paper proposes a novel dimensionality reduction algorithm named ordinal preserving projection (OPP) for learning to rank. We first define two matrices, which work in the row direction and column direction respectively. The two matrices aim at leveraging the global structure of the data set and ordinal information of the observations. By maximizing the corresponding objective functions, we can obtain two optimal projection matrices mapping original data points into low-dimensional subspace, in which both global structure and ordinal information can be preserved. The experiments are conducted on the public available MSRA-MM image data set and "Web Queries" image data set, and the experimental results demonstrate the effectiveness of the proposed method.