CMC: A consensus multi-view clustering model for predicting Alzheimer’s disease progression

Zhang, Xiaobo; Yang, Yan; Li, Tianrui; Zhang, Shujun; Wang, Hao; Fujita, Hamido

doi:10.1016/j.cmpb.2020.105895

Cited by 72 publications

(19 citation statements)

References 41 publications

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“…Then, the view space of the samples is converted to the sample space to the extent that one can learn the sharing and complementarity characteristics of multitask multi-view; however, this method requires additional clustering steps. Zhang, Yang et al [41] designed a multi-view clustering algorithm based on non-negative matrix factorization, which can make full use of limited images to obtain the characteristics of the data and can handle the similarity relationship well between different objects. It successfully solved the disadvantage of setting parameters when exploring multi-view.…”

Section: Related Workmentioning

confidence: 99%

“…However, the above method [38][39][40][41] belongs to the multiview clustering problem. For feature extraction and fusion of views, Hayashi et al [42] presented a one-class classification model with high performance and low complexity, which uses the convolutional image transformation network to convert input images into target images and avoids the output diversity of the classification network and the process of extracting features extensively.…”

Section: Related Workmentioning

confidence: 99%

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Multi-view attention-convolution pooling network for 3D point cloud classification

Wang

Cai

2021

Appl Intell

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Classifying 3D point clouds is an important and challenging task in computer vision. Currently, classification methods using multiple views lose characteristic or detail information during the representation or processing of views. For this reason, we propose a multi-view attention-convolution pooling network framework for 3D point cloud classification tasks. This framework uses Res2Net to extract the features from multiple 2D views. Our attention-convolution pooling method finds more useful information in the input data related to the current output, effectively solving the problem of feature information loss caused by feature representation and the detail information loss during dimensionality reduction. Finally, we obtain the probability distribution of the model to be classified using a full connection layer and the softmax function. The experimental results show that our framework achieves higher classification accuracy and better performance than other contemporary methods using the ModelNet40 dataset.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multi-view attention-convolution pooling network for 3D point cloud classification

Wang

Cai

2021

Appl Intell

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“…For example, those based on hierarchical, partitioning and model-based clustering algorithms/methods ( Dong et al, 2016 ; Racine et al, 2016 ; Dong et al, 2017 ; ten Kate et al, 2018 ; Young et al, 2018 ). Moreover, various machine learning and other statistical approaches have been proposed for both disease progression, prediction and subgroup identification in Alzheimer’s disease ( Fiot et al, 2014 ; Schmidt-Richberg et al, 2016 ; Cheng et al, 2017 ; Bhagwat et al, 2018 ; Khanna et al, 2018 ; de Jong et al, 2019 ; Martí-Juan et al, 2019 ; Brand et al, 2020 ; Golriz Khatami et al, 2020 ; Lei et al, 2020 ; Martí-Juan et al, 2020 ; Lin et al, 2021 ; Zhang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer’s Dementia Longitudinal Cohort

et al. 2021

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A key challenge for the secondary prevention of Alzheimer’s dementia is the need to identify individuals early on in the disease process through sensitive cognitive tests and biomarkers. The European Prevention of Alzheimer’s Dementia (EPAD) consortium recruited participants into a longitudinal cohort study with the aim of building a readiness cohort for a proof-of-concept clinical trial and also to generate a rich longitudinal data-set for disease modelling. Data have been collected on a wide range of measurements including cognitive outcomes, neuroimaging, cerebrospinal fluid biomarkers, genetics and other clinical and environmental risk factors, and are available for 1,828 eligible participants at baseline, 1,567 at 6 months, 1,188 at one-year follow-up, 383 at 2 years, and 89 participants at three-year follow-up visit. We novelly apply state-of-the-art longitudinal modelling and risk stratification approaches to these data in order to characterise disease progression and biological heterogeneity within the cohort. Specifically, we use longitudinal class-specific mixed effects models to characterise the different clinical disease trajectories and a semi-supervised Bayesian clustering approach to explore whether participants can be stratified into homogeneous subgroups that have different patterns of cognitive functioning evolution, while also having subgroup-specific profiles in terms of baseline biomarkers and longitudinal rate of change in biomarkers.

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“…Consensus Multi-view Clustering (CMC) [102] is a nonnegative matrix factorization (NMF) multiview clustering method, which adds regularization for NMF and learns a more robust NMF with the aid of binary relationship matrix.…”

Section: Multiview Clusteringmentioning

confidence: 99%

Extreme learning machine for classification and clustering on multiview data

Chen¹

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CMC: A consensus multi-view clustering model for predicting Alzheimer’s disease progression

Cited by 72 publications

References 41 publications

Multi-view attention-convolution pooling network for 3D point cloud classification

Multi-view attention-convolution pooling network for 3D point cloud classification

Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer’s Dementia Longitudinal Cohort

Extreme learning machine for classification and clustering on multiview data

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