A Classification Algorithm Based on Discriminative Transfer Feature Learning for Early Diagnosis of Alzheimer's Disease

被引：0

作者：

Cui, Xinchun ^{[1
]}

Liu, Yonglin ^{[1
]}

Du, Jianzong ^{[2
]}

Sheng, Qinghua ^{[3
]}

Zheng, Xiangwei ^{[4
]}

Feng, Yue ^{[5
]}

Zhuang, Liying ^{[6
]}

Cui, Xiuming ^{[1
]}

Wang, Jing ^{[1
]}

Liu, Xiaoli ^{[6
]}

机构：

[1] Qufu Normal Univ, Sch Comp Sci, Rizhao 276826, Peoples R China

[2] Zhejiang Hosp, Dept Resp Med, Hangzhou 310013, Peoples R China

[3] Rizhao Cent Hosp, Pharm Dept, Rizhao 276800, Peoples R China

[4] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250358, Peoples R China

[5] Zhejiang Hosp, Dept Radiol, Hangzhou 310013, Peoples R China

[6] Zhejiang Hosp, Dept Neurol, Hangzhou 310013, Peoples R China

来源：

INTELLIGENT COMPUTING THEORIES AND APPLICATION (ICIC 2022), PT I | 2022年 / 13393卷

关键词：

AD; Mild cognitive impairment; Transfer learning; SVM; REGULARIZATION;

D O I：

10.1007/978-3-031-13870-6_34

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper proposes a discriminative transfer feature learning method for MCI conversion prediction using data from the target domain and the auxiliary domain. A transfer component analysis method based on the Maximum Mean Discrepancy (MMD) is proposed at first, which is used to weaken the difference of data distribution between the relevant domain and the target domain. Next, the discriminant optimization term is added to measure the correlation between the sample categories and the sample features of the auxiliary domain, and to improve the inter-class separability of the algorithm. Finally, the support vector machine (SVM) is used to classify MCI patients.

引用

页码：412 / 419

页数：8

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