MTC: A Fast and Robust Graph-Based Transductive Learning Method

Zhang, Yanming; Huang, Kaizhu; Geng, Guanggang; Liu, Cheng‐Lin

doi:10.1109/tnnls.2014.2363679

Cited by 27 publications

(16 citation statements)

References 26 publications

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“…The k-NN method, for example, has complexity order of O(D |V| log |V|) using multidimensional binary search tree [21]. Transductive SVM [7] C |V| 3 Local and Global Consistency [22] |V| 3 Large Scale Transductive SVM [23] C |V| 2 Dynamic Label Propagation [24] |V| 2 Label Propagation [25] |V| 2 Original Particle Competition [17] C 2 |V| + C |E| Labeled Component Unfolding C |V| + C |E| Minimum Tree Cut [26] |V|…”

Section: B Computational Complexity and Running Timementioning

confidence: 99%

Network Unfolding Map by Vertex-Edge Dynamics Modeling

Verri¹,

Urio²,

Zhao³

2018

IEEE Trans. Neural Netw. Learning Syst.

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Abstract-The emergence of collective dynamics in neural networks is a mechanism of the animal and human brain for information processing. In this paper, we develop a computational technique using distributed processing elements in a complex network, which are called particles, to solve semi-supervised learning problems. Three actions govern the particles' dynamics: generation, walking, and absorption. Labeled vertices generate new particles that compete against rival particles for edge domination. Active particles randomly walk in the network until they are absorbed by either a rival vertex or an edge currently dominated by rival particles. The result from the model evolution consists of sets of edges arranged by the label dominance. Each set tends to form a connected subnetwork to represent a data class. Although the intrinsic dynamics of the model is a stochastic one, we prove there exists a deterministic version with largely reduced computational complexity; specifically, with linear growth. Furthermore, the edge domination process corresponds to an unfolding map in such way that edges "stretch" and "shrink" according to the vertexedge dynamics. Consequently, the unfolding effect summarizes the relevant relationships between vertices and the uncovered data classes. The proposed model captures important details of connectivity patterns over the vertex-edge dynamics evolution, in contrast to previous approaches which focused on only vertex or only edge dynamics. Computer simulations reveal that the new model can identify nonlinear features in both real and artificial data, including boundaries between distinct classes and overlapping structures of data.

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Section: B Computational Complexity and Running Timementioning

confidence: 99%

Network Unfolding Map by Vertex-Edge Dynamics Modeling

Verri¹,

Urio²,

Zhao³

2018

IEEE Trans. Neural Netw. Learning Syst.

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“…Graph-based transductive learning is widely used in image retrieval, image segmentation, data clustering and classification (Huang et al, 2014; Liu and Chang, 2009; Wang et al, 2014a; Zhang et al, 2015). For example, a fast and robust graph-based transductive learning method was proposed in (Zhang et al, 2015) by using a minimum tree cut, which was designed for large-scale web-spam detection and interactive image segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…For example, a fast and robust graph-based transductive learning method was proposed in (Zhang et al, 2015) by using a minimum tree cut, which was designed for large-scale web-spam detection and interactive image segmentation. Also, graph-based transductive learning methods have been investigated with great success in medical imaging area (Gao et al, 2015; Kim et al, 2013; Tong et al, 2015), since it can overcome the above difficulties by taking advantage of the data representation on unlabeled testing subjects.…”

Section: Introductionmentioning

confidence: 99%

“…Typically, there are two separate steps in graph-based transductive learning methods: (1) construct the graph, where the vertices represent the labeled and unlabeled samples and the edges reflect the similarity degree between two connected samples (Zhu et al, 2005); and (2) propagate labels from labeled samples to unlabeled samples. Many current label propagation strategies have been proposed to determine the latent labels of testing subjects based on the inter-subject relationships encoded in the graph (Wang and Tsotsos, 2016; Zhang et al, 2015). …”

Section: Introductionmentioning

confidence: 99%

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Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning

Wang

Zhu

Adeli

et al. 2017

Medical Image Analysis

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Graph-based transductive learning (GTL) is a powerful machine learning technique that is used when sufficient training data is not available. In particular, conventional GTL approaches first construct a fixed inter-subject relation graph that is based on similarities in voxel intensity values in the feature domain, which can then be used to propagate the known phenotype data (i.e., clinical scores and labels) from the training data to the testing data in the label domain. However, this type of graph is exclusively learned in the feature domain, and primarily due to outliers in the observed features, may not be optimal for label propagation in the label domain. To address this limitation, a progressive GTL (pGTL) method is proposed that gradually finds an intrinsic data representation that more accurately aligns imaging features with the phenotype data. In general, optimal feature-to-phenotype alignment is achieved using an iterative approach that: (1) refines inter-subject relationships observed in the feature domain by using the learned intrinsic data representation in the label domain, (2) updates the intrinsic data representation from the refined inter-subject relationships, and (3) verifies the intrinsic data representation on the training data to guarantee an optimal classification when applied to testing data. Additionally, the iterative approach is extended to multi-modal imaging data to further improve pGTL classification accuracy. Using Alzheimer’s disease and Parkinson’s disease study data, the classification accuracy of the proposed pGTL method is compared to several state-of-the-art classification methods, and the results show pGTL can more accurately identify subjects, even at different progression stages, in these two study data sets.

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“…Using these connections, the labels can be propagated throughout the graph until all latent labels are determined. Many current label propagation strategies have been proposed to determine the latent labels of testing subjects based on subject-wise relationships encoded in the graph [6]. …”

Section: Introductionmentioning

confidence: 99%

Progressive Graph-Based Transductive Learning for Multi-modal Classification of Brain Disorder Disease

Wang

Zhu

Adeli

et al. 2016

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

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Graph-based Transductive Learning (GTL) is a powerful tool in computer-assisted diagnosis, especially when the training data is not sufficient to build reliable classifiers. Conventional GTL approaches first construct a fixed subject-wise graph based on the similarities of observed features (i.e., extracted from imaging data) in the feature domain, and then follow the established graph to propagate the existing labels from training to testing data in the label domain. However, such a graph is exclusively learned in the feature domain and may not be necessarily optimal in the label domain. This may eventually undermine the classification accuracy. To address this issue, we propose a progressive GTL (pGTL) method to progressively find an intrinsic data representation. To achieve this, our pGTL method iteratively (1) refines the subject-wise relationships observed in the feature domain using the learned intrinsic data representation in the label domain, (2) updates the intrinsic data representation from the refined subject-wise relationships, and (3) verifies the intrinsic data representation on the training data, in order to guarantee an optimal classification on the new testing data. Furthermore, we extend our pGTL to incorporate multi-modal imaging data, to improve the classification accuracy and robustness as multi-modal imaging data can provide complementary information. Promising classification results in identifying Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI), and Normal Control (NC) subjects are achieved using MRI and PET data.

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MTC: A Fast and Robust Graph-Based Transductive Learning Method

Cited by 27 publications

References 26 publications

Network Unfolding Map by Vertex-Edge Dynamics Modeling

Network Unfolding Map by Vertex-Edge Dynamics Modeling

Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning

Progressive Graph-Based Transductive Learning for Multi-modal Classification of Brain Disorder Disease

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