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[1]杨晨晖*,侯超群.一种用于阿尔茨海默病分类的二阶段多任务特征选择算法[J].厦门大学学报(自然科学版),2018,57(05):708-714.[doi:10.6043/j.issn.0438-0479.201801005]
 YANG Chenhui*,HOU Chaoqun.A Two-stage Multi-task Feature Selection Algorithm for Classification of Alzheimer’s Disease[J].Journal of Xiamen University(Natural Science),2018,57(05):708-714.[doi:10.6043/j.issn.0438-0479.201801005]
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一种用于阿尔茨海默病分类的二阶段多任务特征选择算法(PDF/HTML)
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《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
57卷
期数:
2018年05期
页码:
708-714
栏目:
研究论文
出版日期:
2018-09-27

文章信息/Info

Title:
A Two-stage Multi-task Feature Selection Algorithm for Classification of Alzheimer’s Disease
文章编号:
0438-0479(2018)05-0708-07
作者:
杨晨晖*侯超群
厦门大学信息科学与技术学院,福建 厦门 361005
Author(s):
YANG Chenhui*HOU Chaoqun
School of Information Science and Engineering,Xiamen University,Xiamen 361005,China
关键词:
阿尔茨海默病 类内方差 有效距离 多任务特征选择 拉普拉斯分数
Keywords:
Alzheimer’s disease intra-class variance effective distance multitask feature selection Laplacian score
分类号:
TP 391
DOI:
10.6043/j.issn.0438-0479.201801005
文献标志码:
A
摘要:
阿尔茨海默病(Alzheimer’s disease,AD)具有数据量少、多模态以及高维度等特点.为了对AD进行有效的预测,首先提出一个基于类内方差最小化的多任务特征选择(minimum intra-class variance-based multitask feature selection,MIVMTFS)算法,然后结合基于有效距离的拉普拉斯分数特征选择(effective distance-based laplacian score feature selection,EDLSFS)算法和MIVMTFS算法,提出一种二阶段多任务特征选择(two-stage multi-task feature selection,TSMTFS)算法.TSMTFS算法先利用EDLSFS算法在保持特征局部结构的情况下对原始样本特征进行无监督预降维,再利用MIVMTFS算法对降维后的特征进行有监督地再降维,最终获得一个精简特征子集.实验部分主要包括AD的2个二分类任务,并分别对单模态数据和多模态数据进行实验.实验结果验证了TSMTFS算法在AD领域能够缓解单模态特征选择的信息不够充分、样本量少以及特征维度高等不足.
Abstract:
Alzheimer’s disease(AD)is characterized with few samples,multi modes,and high dimensionality.In this article,we first propose a minimum intra-class variance-based multitask feature selection(MIVMTFS)algorithm.Then,we propose a multi-task feature selection method of two-stage strategy based on combining effective distance-based Laplacian score feature selection(EDLSFS)algorithm and MIVMTFS algorithm.The former is used to pre-reduce feature dimension of label data,keeping the local structure of the original sample feature.The latter has been used to reduce the dimensionality of the reduced feature further.Finally,we can obtain a simplified feature subset.The experimental part of this paper includes two binary classification tasks,and we have also used the single modal and multi modal data to test algorithms’ performances.Experimental results show that TSMTFS algorithm can alleviate these shortcomings including information deficiency,few samples and feature of high-dimensional during the feature selection of single modal in the field of AD.

参考文献/References:

[1] BROOKMEYER R,JOHNSON E,ZIEGLER-GRAHAM K,et al.Forecasting the global burden of Alzheimer’s disease[J].Alzheimer’s & Dementia,2007,3(3):186-191.
[2] MARCUS D S,FOTENOS A F,CSERNANSKY J G,et al.Open access series of imaging studies:longitudinal MRI data in non-demented and demented older adults[J].Journal of Cognitive Neuroscience,2010,22(12):2677-2684.
[3] KL?PPEL S,STONNINGTON C M,BARNES J,et al.Accuracy of dementia diagnosis:a direct comparison between radiologists and a computerized method[J].Brain,2008,131(11):2969-2974.
[4] LIU M,ZHANG D.Feature selection with effective distance[J].Neurocomputing,2016,215:100-109.
[5] ZHU P,ZUO W,ZHANG L,et al.Unsupervised feature selection by regularized self-representation[J].Pattern Recognition,2015,48(2):438-446.
[6] TANG J,ZHANG J,YAO L,et al.ArnetMiner:extraction and mining of academic social networks[C]∥Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Las Vegas:ACM,2008:990-998.
[7] LIU F,WEE C Y,CHEN H,et al.Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer’s disease and mild cognitive impairment identification[J].Neuroimage,2014,84:466-475.
[8] LIU S,LIU S,CAI W,et al.Early diagnosis of Alzheimer’s disease with deep learning[C]∥2014 IEEE 11th International Symposium on Biomedical Imaging(ISBI).Beijing:IEEE,2014:1015-1018.
[9] SALAKHUTDINOV R,HINTON G E.Replicated softmax:an undirected topic model[C]∥International Conference on Neural Information Processing Systems.Vancouver:MIT Press,2010:1607-1614.
[10] GUPTA A,MAIDA A S,AYHAN M.Natural image bases to represent neuroimaging data[C]∥International Conference on Machine Learning.Beijing:ACM,2014:987-994.
[11] PAYAN A,MONTANA G.Predicting Alzheimer’s disease:a neuroimaging study with 3D convolutional neural networks[EB/OL].[2017-12-30].https:∥arxiv.org/abs/1502.02506.
[12] BELHUMEUR P N,HESPANHA J P,KRIEGMAN D J.Eigenfaces vs.fisherfaces:recognition using class specific linear projection[C]∥European Conference on Computer Vision.Buxton:Springer,1996:45-58.
[13] WOLD S,ESBENSEN K,GELADI P.Principal component analysis[J].Chemometrics and Intelligent Laboratory Systems,1987,2(1):37-52.
[14] HUANG S,LI J,YE J,et al.Identifying Alzheimer’s disease-related brain regions from multi-modality neuroimaging data using sparse composite linear discrimination analysis[J].Advances in Neural Information Processing Systems,2011:1431-1439.doi:10.1016/0169-7439(87)80084-9.
[15] ZHANG D,SHEN D.Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease[J].NeuroImage,2012,59(2):895-907.
[16] JIE B,ZHANG D,CHENG B,et al.Manifold regularized multitask feature selection for multimodality classification in Alzheimer’s disease[C]∥International Conference on Medical Image Computing and Computer-assisted Intervention.Heidelberg:Springer,2013:275-283.
[17] BELKIN M,NIYOGI P.Laplacian eigenmaps and spectral techniques for embedding and clustering[J].Advances in Neural Information Processing Systems,2001,14(6):585-591.
[18] HE X,NIYOGI P.Locality preserving projections(LPP)[J].Advances in Neural Information Processing Systems,2002,16(1):186-197.
[19] CHUNG F R K.Spectral graph theory(CBMS regional conference series in mathematics)[M].Rhode Island:American Mathematical Society,1997:92.
[20] MITRA P,MURTHY C A,Pal S K.Unsupervised feature selection using feature similarity[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(3):301-312.
[21] HE X,CAI D,NIYOGI P.Laplacian score for feature selection[C]∥International Conference on Neural Information Processing Systems.Vancouver,British Columbia:MIT Press,2005:507-514.
[22] ZHAO B,KWOK J,WANG F,et al.Unsupervised maximum margin feature selection with manifold regularization[C]∥IEEE Conference on Computer Vision and Pattern Recognition.Miami:IEEE,2009:888-895.
[23] CAI D,ZHANG C,HE X.Unsupervised feature selection for multi-cluster data[C]∥ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Washington DC:ACM,2010:333-342.
[24] ZHAO Z,WANG L,LIU H,et al.On similarity preserving feature selection[J].IEEE Transactions on Knowledge and Data Engineering,2013,25(3):619-632.
[25] YANG S,HOU C,NIE F,et al.Unsupervised maximum margin feature selection via L2,1-norm minimization[J].Neural Computing and Applications,2012,21(7):1791-1799.
[26] BROCKMANN D,HELBING D.The hidden geometry of complex,network-driven contagion phenomena[J].Science,2013,342(6164):1337-1342.
[27] ZHANG D,WANG Y,ZHOU L,et al.Multimodal classification of Alzheimer’s disease and mild cognitive impairment[J].Neuroimage,2011,55(3):856-867.

备注/Memo

备注/Memo:
收稿日期:2018-01-04 录用日期:2018-06-20
*通信作者:chyang@xmu.edu.cn
引文格式:杨晨晖,侯超群.一种用于阿尔茨海默病分类的二阶段多任务特征选择算法[J].厦门大学学报(自然科学版),2018,57(5):708-714.
Citation:YANG C H,HOU C Q.A two-stage multi-task feature selection algorithm for classification of Alzheimer’s disease[J].J Xiamen Univ Nat Sci,2018,57(5):708-714.(in Chinese)
更新日期/Last Update: 1900-01-01