Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive dimension reduction using discriminant analysis and K-means clustering
Proceedings of the 24th international conference on Machine learning
Enhancing semi-supervised clustering: a feature projection perspective
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Locality sensitive semi-supervised feature selection
Neurocomputing
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Discriminatively regularized least-squares classification
Pattern Recognition
Transductive Component Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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In this paper, in terms of pairwise constraints which specify whether a pair of instances belong to the same class (must-link constraints) or different classes (cannot-link constraints), we propose a novel semi-supervised discriminant analysis algorithm which integrates both global and local structures. Specifically, our objective is to learn a smooth as well as discriminative subspace. In order to achieve it, we jointly use both the instances in the cannot-link constraints to maximize the separability between different classes while applying those in the must-link constraints to minimize the distance between the same class and the integration of global and local structures of the data to make nearby instances in the original space close to each other in the embedding space. Experimental results on a collection of real-world data sets demonstrated the effectiveness of the proposed algorithm.