TY - JOUR
T1 - Robust nonlinear multimodal classification of Alzheimer's disease based on GMM
AU - Ziyue Wang
JO - Journal of Information and Computing Science
VL - 1
SP - 016
EP - 021
PY - 2024
DA - 2024/01
SN - 15
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22393.html
KW - Robust nonlinear regression, Outlier, Kernel method, Classification. 

AB -  Accurate  diagnosis  of  Alzheimer's  disease  (AD)  and  its  prodromal  stage  mild  cognitive 
impairment  (MCI)  is  very  important  for  patients  and  clinicians.  There  are  many  useful  medical  data  have 
been discovered to be remarkable for diagnosis i.e., structural MR imaging (MRI), functional imaging (e.g., 
FDG-PET and FIB-PET). Multimodal classification model is needed to combine these biomarkers to improve 
the  diagnose  performance.  Some  methods  have  been  proposed  such  as  linear  mixed  kernel,  combined 
embedding and nonlinear graph fusion. These methods have efficiently employed the multimodal data, but 
they ignore the influence of noise and outliers. Noise is easily generated in image analysis and measurement. 
To enhance robustness, mixture distributions were applied in nonlinear regression models. Gaussian mixture 
model  is  successfully  applied  in  many  domains.  In  this  paper,  we  generalize  nonlinear  multimodal 
classification model based on GMM. The performance on real dataset: 22 AD, 23 MCI and 25 NC (health) is 
comparable to other methods.