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2016 | OriginalPaper | Buchkapitel

Stability-Weighted Matrix Completion of Incomplete Multi-modal Data for Disease Diagnosis

verfasst von : Kim-Han Thung, Ehsan Adeli, Pew-Thian Yap, Dinggang Shen

Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

Verlag: Springer International Publishing

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Abstract

Effective utilization of heterogeneous multi-modal data for Alzheimer’s Disease (AD) diagnosis and prognosis has always been hampered by incomplete data. One method to deal with this is low-rank matrix completion (LRMC), which simultaneous imputes missing data features and target values of interest. Although LRMC yields reasonable results, it implicitly weights features from all the modalities equally, ignoring the differences in discriminative power of features from different modalities. In this paper, we propose stability-weighted LRMC (swLRMC), an LRMC improvement that weights features and modalities according to their importance and reliability. We introduce a method, called stability weighting, to utilize subsampling techniques and outcomes from a range of hyper-parameters of sparse feature learning to obtain a stable set of weights. Incorporating these weights into LRMC, swLRMC can better account for differences in features and modalities for improving diagnosis. Experimental results confirm that the proposed method outperforms the conventional LRMC, feature-selection based LRMC, and other state-of-the-art methods.

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Fußnoten
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Metadaten
Titel
Stability-Weighted Matrix Completion of Incomplete Multi-modal Data for Disease Diagnosis
verfasst von
Kim-Han Thung
Ehsan Adeli
Pew-Thian Yap
Dinggang Shen
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-46723-8_11