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2016 | OriginalPaper | Buchkapitel

Joint Discriminative and Representative Feature Selection for Alzheimer’s Disease Diagnosis

verfasst von : Xiaofeng Zhu, Heung-Il Suk, Kim-Han Thung, Yingying Zhu, Guorong Wu, Dinggang Shen

Erschienen in: Machine Learning in Medical Imaging

Verlag: Springer International Publishing

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Abstract

Neuroimaging data have been widely used to derive possible biomarkers for Alzheimer’s Disease (AD) diagnosis. As only certain brain regions are related to AD progression, many feature selection methods have been proposed to identify informative features (i.e., brain regions) to build an accurate prediction model. These methods mostly only focus on the feature-target relationship to select features which are discriminative to the targets (e.g., diagnosis labels). However, since the brain regions are anatomically and functionally connected, there could be useful intrinsic relationships among features. In this paper, by utilizing both the feature-target and feature-feature relationships, we propose a novel sparse regression model to select informative features which are discriminative to the targets and also representative to the features. We argue that the features which are representative (i.e., can be used to represent many other features) are important, as they signify strong “connection” with other ROIs, and could be related to the disease progression. We use our model to select features for both binary and multi-class classification tasks, and the experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show that the proposed method outperforms other comparison methods considered in this work.

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Fußnoten
1
Note that since a vector \(\mathbf {x}_{i}\) in the observation \(\mathbf {X}\) can be used to represent itself, there always exists a feasible (trivial) solution. That is, its corresponding coefficient in \(\mathbf {S}\) equals to one and all the other coefficients equal to zero. However, due to our assumption of dependencies among ROIs, i.e.,  \(rank(\mathbf {X})< \mathrm {min}(n,d)\), where \(rank(\mathbf {X})\) indicates the rank of the matrix \(\mathbf {X}\), there also exist non-trial solutions in the space of \(\mathbf {I} - null(\mathbf {X})\) [7], where \(null(\mathbf {X})\) stands for the null space of \(\mathbf {X}\).
 
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Metadaten
Titel
Joint Discriminative and Representative Feature Selection for Alzheimer’s Disease Diagnosis
verfasst von
Xiaofeng Zhu
Heung-Il Suk
Kim-Han Thung
Yingying Zhu
Guorong Wu
Dinggang Shen
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-47157-0_10