Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Rectified nearest feature line segment for pattern classification
Pattern Recognition
A generalization of dissimilarity representations using feature lines and feature planes
Pattern Recognition Letters
Graph Classification Based on Dissimilarity Space Embedding
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Recently, generalized dissimilarity representations have shown their potential for small sample size problems. In generalizations by feature lines, instead of dissimilarities with objects, we have dissimilarities with feature lines. One drawback of such generalization is the high amount of generated lines that increases computational costs and may provide redundant information. To overcome this, the selection of lines based on the length of the line segments has been considered in previous works, showing good results for correlated data. In this paper, we propose a new supervised criterion for the selection of feature lines. Experimental results show that the proposed criterion obtains competitive or better results than those obtained by previous criteria, especially for data with high intrinsic dimension, spherical data and data with outliers. As our proposal provides better results for small representation sets, it allows one to obtain a good trade-off between classification accuracy and computational efficiency.