[1]WEI Caifeng,SUN Yongcong,ZENG Xianhua.Dictionary pair learning with graph regularization for mild cognitive impairment prediction[J].CAAI Transactions on Intelligent Systems,2019,14(2):369-377.[doi:10.11992/tis.201709033]
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Dictionary pair learning with graph regularization for mild cognitive impairment prediction

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 2 Page number: 369-377 Column: 学术论文—机器学习 Public date: 2019-03-05
Title:
Dictionary pair learning with graph regularization for mild cognitive impairment prediction
Author(s):
WEI Caifeng1; 2;  SUN Yongcong1; 2;  ZENG Xianhua1; 2
1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunication, Chongqing 400065, China;
2. Chongqing Key Laboratory of Computation Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Keywords:
graph regularization; dictionary pair learning; geometric neighborhood relationship; image classification; mild cognitive impairment prediction
CLC:
TP391;R749
DOI:
10.11992/tis.201709033
Abstract:
Aiming at dictionary pair learning (DPL) methods only consider the reconstruction error of a sub-dictionary from the same class and the sparseness of coefficients from different classes, and do not consider the geometric neighborhood topological relationships between images. To improve the classification ability of DPL algorithms, we propose a DPL with graph regularization (GDPL) algorithm based on geometric neighborhood topological relationships. This algorithm is based on the idea that keeping the neighborhood relationship makes the distance between the neighborhood projection coefficients of the same kind small, while the distance between projection coefficients of different kinds is large. Experiments on mild cognitive impairment prediction using the ADNI1 dataset show that the coding coefficient learned from the GDPL algorithm is 2%~6% higher than that which uses the combined biomarker as feature prediction, according to accuracy (ACC) and area under curve (AUC) metrics. Moreover, the experimental result obtained using GDPL is also better than that obtained using DPL algorithm.
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