Chapter

Distance Metric Learning as Feature Reduction Technique for the Alzheimer’s Disease Diagnosis

05/2011; DOI:10.1007/978-3-642-21326-7_8 pp.68-76

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.

KeywordsSPECT Brain Imaging–Alzheimer’s disease–Distance Metric Learning–feature reduction–Support Vector Machines

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Keywords

97.93% AD diagnosis accuracy
 
detection
 
different classes
 
distance metric technique
 
effective feature reduction technique
 
KeywordsSPECT Brain Imaging–Alzheimer’s disease–Distance Metric Learning–feature reduction–Support Vector Machines
 
Neighbors
 
Normalized Minimum Squared Error
 
novel classification method
 
PCA
 
RECT-LMNN
 
rectangular matrix
 
reports clear improvements
 
SPECT images
 
square error
 
Support Vector Machine
 
techniques
 
template-based normalized