Local distance preservation in the GP-LVM through back constraints
ICML '06 Proceedings of the 23rd international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Discriminative Gaussian process latent variable model for classification
Proceedings of the 24th international conference on Machine learning
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Multi-view clustering via canonical correlation analysis
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Multi-view regression via canonical correlation analysis
COLT'07 Proceedings of the 20th annual conference on Learning theory
Shared Kernel Information Embedding for Discriminative Inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
Assistive tagging: A survey of multimedia tagging with human-computer joint exploration
ACM Computing Surveys (CSUR)
Quality of information-based source assessment and selection
Neurocomputing
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In image classification, the goal is to decide whether an image belongs to a certain category or not. Multiple features are usually employed to comprehend the contents of images substantially for the improvement of classification accuracy. However, it also brings in some new problems that how to effectively combine multiple features together, and how to handle the high-dimensional features from multiple views given the small training set. In this paper, we present a large-margin Gaussian process approach to discover the latent space shared by multiple features. Therefore, multiple features can complement each other in this low-dimensional latent space, which derives a strong discriminative ability from the large-margin principle, and then the following classification task can be effectively accomplished. The resulted objective function can be efficiently solved using the gradient descent techniques. Finally, we demonstrate the advantages of the proposed algorithm on real-world image datasets for discovering discriminative latent space and improving the classification performance.