Introduction to algorithms
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Efficient Pattern Recognition Using a New Transformation Distance
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Selection of the optimal parameter value for the Isomap algorithm
Pattern Recognition Letters
ViSOM for Dimensionality Reduction in Face Recognition
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Nonlinear dimensionality reduction for face recognition
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Linear and nonlinear dimensionality reduction for face recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Classifying faces with discriminant isometric feature mapping
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
On nonlinear dimensionality reduction for face recognition
Image and Vision Computing
Novel Fisher discriminant classifiers
Pattern Recognition
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The Isomap method has demonstrated promising results in finding low dimensional manifolds from data points in the high dimensional input space. While classical subspace methods use Euclidean or Manhattan metrics to represent distances between data points and apply Principal Component Analysis to induce linear manifolds, the Isomap method estimates geodesic distances between data points and then uses Multi-Dimensional Scaling to induce low dimensional manifolds. Since the Isomap method is developed based on reconstruction principle, it may not be optimal from the classification viewpoint. In this paper, we present an extended Isomap method that utilizes Fisher Linear Discriminant for pattern classification. Numerous experiments on image data sets show that our extension is more effective than the original Isomap method for pattern classification. Furthermore, the extended Isomap method shows promising results compared with best methods in the face recognition literature.