Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Spectral clustering and transductive learning with multiple views
Proceedings of the 24th international conference on Machine learning
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Graph-based semi-supervised learning with multiple labels
Journal of Visual Communication and Image Representation
Semi-supervised learning with very few labeled training examples
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Multilabel dimensionality reduction via dependence maximization
ACM Transactions on Knowledge Discovery from Data (TKDD)
Unified tag analysis with multi-edge graph
Proceedings of the international conference on Multimedia
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-modal constraint propagation for heterogeneous image clustering
MM '11 Proceedings of the 19th ACM international conference on Multimedia
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Learning hash functions for cross-view similarity search
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Semantic context learning with large-scale weakly-labeled image set
Proceedings of the 21st ACM international conference on Information and knowledge management
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In many applications, the data may be high dimensional, represented by multiple features, and associated with more than one labels. Embedding learning is an effective strategy for dimensionality reduction and for nearest neighbor search in massive datasets. We propose a novel method to seek compact embedding that allows efficient retrieval with incompletely-labeled multi-view data. Based on multi-graph Laplacian, we achieve the optimal combination of heterogeneous features to effectively describe data, which exploits the feature correlations between different views. We learn the embedding that preserves the neighborhood context in the original spaces, and obtain the complete labels simultaneously. Inter-label correlations are sufficiently leveraged in the proposed framework. Our goal is to find the maps from multiple input spaces to the compact embedding space and to the semantic concept space at the same time. There is semantic gap between the input multi-view feature spaces and the semantic concept space; and the compact embedding space can be looked on as the bridge between the above spaces. Experimental evaluation on three real-world datasets demonstrates the effectiveness of the proposed method.