MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Learning a Similarity Metric Discriminatively, with Application to Face Verification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
A First Study on the Use of Coevolutionary Algorithms for Instance and Feature Selection
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
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In this paper we present a novel classification method of SPECT images for the early diagnosis of the Alzheimer's disease (AD). The proposed method is based on distance metric learning classification with the Large Margin Nearest Neighbour algorithm (LMNN) aiming to separate examples from different classes (Normal and AD) by a large margin. In particular, we show how to learn a Mahalanobis distance for k-nearest neighbors (KNN) classification. It is also introduced the concept of energy-based model which outperforms both Mahalanobis and Euclidean distances. The system combines firstly Normalized Minimum Square Error (NMSE) and t-test selection with secondly Kernel Principal Components Analysis (KPCA) to find the main features. Applying KPCA trick in the feature extraction, LMNN turns into Kernel-LMNN (KLMNN) with better results than the first. KLMNN reachs results of accuracy=96.91%, sensitivity=100%, specificity=95.35% outperforming other recently reported methods such as Principal Component Analysis( PCA) in combination with Linear Discriminant Analysis (LDA) evaluated with Support Vector Machines (SVM) or linear SVM.