Multi-layer multi-view classification for Alzheimer's disease diagnosis

Changqing Zhang, Ehsan Adeli, Tao Zhou, Xiaobo Chen, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this paper, we propose a novel multi-view learning method for Alzheimer's Disease (AD) diagnosis, using neuroimaging and genetics data. Generally, there are several major challenges associated with traditional classification methods on multi-source imaging and genetics data. First, the correlation between the extracted imaging features and class labels is generally complex, which often makes the traditional linear models ineffective. Second, medical data may be collected from different sources (i.e., multiple modalities of neuroimaging data, clinical scores or genetics measurements), therefore, how to effectively exploit the complementarity among multiple views is of great importance. In this paper, we propose a Multi-Layer Multi-View Classification (ML-MVC) approach, which regards the multi-view input as the first layer, and constructs a latent representation to explore the complex correlation between the features and class labels. This captures the high-order complementarity among different views, as we exploit the underlying information with a low-rank tensor regularization. Intrinsically, our formulation elegantly explores the nonlinear correlation together with complementarity among different views, and thus improves the accuracy of classification. Finally, the minimization problem is solved by the Alternating Direction Method of Multipliers (ADMM). Experimental results on Alzheimers Disease Neuroimaging Initiative (ADNI) data sets validate the effectiveness of our proposed method.

Original languageEnglish
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAAAI press
Pages4406-4413
Number of pages8
ISBN (Electronic)9781577358008
Publication statusPublished - 2018 Jan 1
Externally publishedYes
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: 2018 Feb 22018 Feb 7

Other

Other32nd AAAI Conference on Artificial Intelligence, AAAI 2018
CountryUnited States
CityNew Orleans
Period18/2/218/2/7

Fingerprint

Neuroimaging
Labels
Imaging techniques
Tensors
Genetics

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Zhang, C., Adeli, E., Zhou, T., Chen, X., & Shen, D. (2018). Multi-layer multi-view classification for Alzheimer's disease diagnosis. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 4406-4413). AAAI press.

Multi-layer multi-view classification for Alzheimer's disease diagnosis. / Zhang, Changqing; Adeli, Ehsan; Zhou, Tao; Chen, Xiaobo; Shen, Dinggang.

32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, 2018. p. 4406-4413.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhang, C, Adeli, E, Zhou, T, Chen, X & Shen, D 2018, Multi-layer multi-view classification for Alzheimer's disease diagnosis. in 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, pp. 4406-4413, 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, New Orleans, United States, 18/2/2.
Zhang C, Adeli E, Zhou T, Chen X, Shen D. Multi-layer multi-view classification for Alzheimer's disease diagnosis. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press. 2018. p. 4406-4413
Zhang, Changqing ; Adeli, Ehsan ; Zhou, Tao ; Chen, Xiaobo ; Shen, Dinggang. / Multi-layer multi-view classification for Alzheimer's disease diagnosis. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, 2018. pp. 4406-4413
@inproceedings{fc35be62728145d59306655e51290ea6,
title = "Multi-layer multi-view classification for Alzheimer's disease diagnosis",
abstract = "In this paper, we propose a novel multi-view learning method for Alzheimer's Disease (AD) diagnosis, using neuroimaging and genetics data. Generally, there are several major challenges associated with traditional classification methods on multi-source imaging and genetics data. First, the correlation between the extracted imaging features and class labels is generally complex, which often makes the traditional linear models ineffective. Second, medical data may be collected from different sources (i.e., multiple modalities of neuroimaging data, clinical scores or genetics measurements), therefore, how to effectively exploit the complementarity among multiple views is of great importance. In this paper, we propose a Multi-Layer Multi-View Classification (ML-MVC) approach, which regards the multi-view input as the first layer, and constructs a latent representation to explore the complex correlation between the features and class labels. This captures the high-order complementarity among different views, as we exploit the underlying information with a low-rank tensor regularization. Intrinsically, our formulation elegantly explores the nonlinear correlation together with complementarity among different views, and thus improves the accuracy of classification. Finally, the minimization problem is solved by the Alternating Direction Method of Multipliers (ADMM). Experimental results on Alzheimers Disease Neuroimaging Initiative (ADNI) data sets validate the effectiveness of our proposed method.",
author = "Changqing Zhang and Ehsan Adeli and Tao Zhou and Xiaobo Chen and Dinggang Shen",
year = "2018",
month = "1",
day = "1",
language = "English",
pages = "4406--4413",
booktitle = "32nd AAAI Conference on Artificial Intelligence, AAAI 2018",
publisher = "AAAI press",

}

TY - GEN

T1 - Multi-layer multi-view classification for Alzheimer's disease diagnosis

AU - Zhang, Changqing

AU - Adeli, Ehsan

AU - Zhou, Tao

AU - Chen, Xiaobo

AU - Shen, Dinggang

PY - 2018/1/1

Y1 - 2018/1/1

N2 - In this paper, we propose a novel multi-view learning method for Alzheimer's Disease (AD) diagnosis, using neuroimaging and genetics data. Generally, there are several major challenges associated with traditional classification methods on multi-source imaging and genetics data. First, the correlation between the extracted imaging features and class labels is generally complex, which often makes the traditional linear models ineffective. Second, medical data may be collected from different sources (i.e., multiple modalities of neuroimaging data, clinical scores or genetics measurements), therefore, how to effectively exploit the complementarity among multiple views is of great importance. In this paper, we propose a Multi-Layer Multi-View Classification (ML-MVC) approach, which regards the multi-view input as the first layer, and constructs a latent representation to explore the complex correlation between the features and class labels. This captures the high-order complementarity among different views, as we exploit the underlying information with a low-rank tensor regularization. Intrinsically, our formulation elegantly explores the nonlinear correlation together with complementarity among different views, and thus improves the accuracy of classification. Finally, the minimization problem is solved by the Alternating Direction Method of Multipliers (ADMM). Experimental results on Alzheimers Disease Neuroimaging Initiative (ADNI) data sets validate the effectiveness of our proposed method.

AB - In this paper, we propose a novel multi-view learning method for Alzheimer's Disease (AD) diagnosis, using neuroimaging and genetics data. Generally, there are several major challenges associated with traditional classification methods on multi-source imaging and genetics data. First, the correlation between the extracted imaging features and class labels is generally complex, which often makes the traditional linear models ineffective. Second, medical data may be collected from different sources (i.e., multiple modalities of neuroimaging data, clinical scores or genetics measurements), therefore, how to effectively exploit the complementarity among multiple views is of great importance. In this paper, we propose a Multi-Layer Multi-View Classification (ML-MVC) approach, which regards the multi-view input as the first layer, and constructs a latent representation to explore the complex correlation between the features and class labels. This captures the high-order complementarity among different views, as we exploit the underlying information with a low-rank tensor regularization. Intrinsically, our formulation elegantly explores the nonlinear correlation together with complementarity among different views, and thus improves the accuracy of classification. Finally, the minimization problem is solved by the Alternating Direction Method of Multipliers (ADMM). Experimental results on Alzheimers Disease Neuroimaging Initiative (ADNI) data sets validate the effectiveness of our proposed method.

UR - http://www.scopus.com/inward/record.url?scp=85060463005&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85060463005&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85060463005

SP - 4406

EP - 4413

BT - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018

PB - AAAI press

ER -