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Graph-Kernel Based Structured Feature Selection for Brain Disease Classification Using Functional Connectivity Networks
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DOI：10.1109/ACCESS.2019.2903332.png)
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摘要
En 中文
Feature selection has been applied to the analysis of complex structured data, such as functional connectivity networks (FCNs) constructed on resting-state functional magnetic resonance imaging (rs-fMRI), for removing redundant/noisy information. Previous studies usually first extract topological measures (e.g., clustering coefficients) from FCNs as feature vectors, and then perform vector-based algorithms (e.g., t-test) for feature selection. However, due to the use of vector-based representations, these methods simply ignore important local-to-global structural information of connectivity networks, while such structural information could be used as prior knowledge of networks to improve the learning performance. To this end, we propose a graph kernel-based structured feature selection (gk-SFS) method for brain disease classification with connectivity networks. Different from previous studies, our proposed gk-SFS method uses the graph kernel technique to calculate the similarity of networks and thus can explicitly take advantage of the structural information of connectivity networks. Specifically, we first develop a new graph kernel-based Laplacian regularizer in our gk-SFS model to preserve the structural information of connectivity networks. We also employ an l(1)-norm based sparsity regularizer to select a small number of discriminative features for brain disease analysis (classification). The experimental results on both ADNI and ADHD-200 datasets with rs-fMRI data demonstrate that the proposed gk-SFS method can further improve the classification performance compared with the state-of-the-art methods.
Keyword:
Functional connectivity network
graph kernel
feature selection
Laplacian regularizer
classification
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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