Pattern recognition: human and mechanical
Pattern recognition: human and mechanical
Matrix analysis
Graph drawing by force-directed placement
Software—Practice & Experience
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Kohonen Maps
Self-Organizing Maps
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
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
Orthogonal Neighborhood Preserving Projections
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Discriminant neighborhood embedding for classification
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
COCOON'03 Proceedings of the 9th annual international conference on Computing and combinatorics
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
The Trace Ratio Optimization Problem for Dimensionality Reduction
SIAM Journal on Matrix Analysis and Applications
Towards collaborative feature extraction for face recognition
Natural Computing: an international journal
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Graph-based methods for linear dimensionality reduction have recently attracted much attention and research efforts. The main goal of these methods is to preserve the properties of a graph representing the affinity between data points in local neighborhoods of the high-dimensional space. It has been observed that, in general, supervised graph-methods outperform their unsupervised peers in various classification tasks. Supervised graphs are typically constructed by allowing two nodes to be adjacent only if they are of the same class. However, such graphs are oblivious to the proximity of data from different classes. In this paper, we propose a novel methodology which builds on 'repulsion graphs', i.e., graphs that model undesirable proximity between points. The main idea is to repel points from different classes that are close by in the input high-dimensional space. The proposed methodology is generic and can be applied to any graph-based method for linear dimensionality reduction. We provide ample experimental evidence in the context of face recognition, which shows that the proposed methodology (i) offers significant performance improvement to various graph-based methods and (ii) outperforms existing solutions relying on repulsion forces.