Distance metric learning as feature reduction technique for the Alzheimer's disease diagnosis

Authors:
R. Chaves;J. Ramírez;J. M. Górriz;D. Salas-Gonzalez;M. López
Affiliations:
University of Granada, Granada, Spain;University of Granada, Granada, Spain;University of Granada, Granada, Spain;University of Granada, Granada, Spain;University of Granada, Granada, Spain
Venue:
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Year:
2011

Citing 9
Cited 1

A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
An Automatic Threshold-Based Scaling Method for Enhancing the Usefulness of Tc-HMPAO SPECT in the Diagnosis of Alzheimer's Disease

MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Large Margin Methods for Structured and Interdependent Output Variables

The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)

Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information

Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Fast solvers and efficient implementations for distance metric learning

Proceedings of the 25th international conference on Machine learning
Learning a Mahalanobis distance metric for data clustering and classification

Pattern Recognition
Distance Metric Learning for Large Margin Nearest Neighbor Classification

The Journal of Machine Learning Research
Principal component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer's disease

Neurocomputing

Application of Empirical Mode Decomposition (EMD) on DaTSCAN SPECT images to explore Parkinson Disease

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a novel classification method of SPECT images for the development of a computer aided diagnosis (CAD) system aiming to improve the early detection of the Alzheimer's Disease (AD). The system combines firstly template-based normalized mean square error (NMSE) features of tridimensional Regions of Interest (ROIs) t-test selected with secondly Large Margin Nearest Neighbors (LMNN), which is a distance metric technique aiming to separate examples from different classes (Controls and AD) by a Large Margin. LMNN uses a rectangular matrix (called RECT-LMNN) as an effective feature reduction technique. Moreover, the proposed system evaluates Support Vector Machine (SVM) classifier, yielding a 97.93% AD diagnosis accuracy, which reports clear improvements over existing techniques, for instance the Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) or Normalized Minimum Squared Error (NMSE) evaluated with SVM.