Kernel principal component analysis
Advances in kernel methods
Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Face Recognition Based on Nearest Linear Combinations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Document Clustering Using Locality Preserving Indexing
IEEE Transactions on Knowledge and Data Engineering
Classification of gene-expression data: The manifold-based metric learning way
Pattern Recognition
Rapid and brief communication: Center-based nearest neighbor classifier
Pattern Recognition
Rectified nearest feature line segment for pattern classification
Pattern Recognition
Face recognition using spectral features
Pattern Recognition
Using a Global Parameter for Gaussian Affinity Matrices in Spectral Clustering
High Performance Computing for Computational Science - VECPAR 2008
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Neighbor-weighted K-nearest neighbor for unbalanced text corpus
Expert Systems with Applications: An International Journal
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Linear Regression for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel classifier based on shortest feature line segment
Pattern Recognition Letters
Maximum Correntropy Criterion for Robust Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Class confidence weighted kNN algorithms for imbalanced data sets
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
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In nearest feature line approach, the representational capacity of a given training set is generalized by defining feature lines passing through each pair of samples belonging to the same class. This technique is shown to provide superior performance on various classification problems than the nearest neighbor approach. From the performance point of view, the major weakness of this technique is the interpolation inaccuracy which occurs when a feature line passes through samples that are far away from each other. Several variants are recently proposed to avoid this weakness. In this study, we follow a different path and propose to transform the training data of different classes into separate clusters before applying nearest feature line classifier. Spectral clustering based transformation is used for this purpose and it is shown that the accuracies achieved by both the nearest feature line and the shortest feature line segment approach which is the most recent variant of the nearest feature line technique are improved.