論文

査読有り
2012年4月

Combining sparseness and smoothness improves classification accuracy and interpretability

NEUROIMAGE
  • Matthew de Brecht
  • ,
  • Noriko Yamagishi

60
2
開始ページ
1550
終了ページ
1561
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1016/j.neuroimage.2011.12.085
出版者・発行元
ACADEMIC PRESS INC ELSEVIER SCIENCE

Sparse logistic regression (SLR) has been shown to be a useful method for decoding high-dimensional fMRI and MEG data by automatically selecting relevant feature dimensions. However, when applied to signals with high spatio-temporal correlations, SLR often over-prunes the feature space, which can result in overfitting and weight vectors that are difficult to interpret. To overcome this problem, we investigate a modification of l(1)-normed sparse logistic regression, called smooth sparse logistic regression (SSLR), which has a spatio-temporal "smoothing" prior that encourages weights that are close in time and space to have similar values. This causes the classifier to select spatio-temporally continuous groups of features, whereas SLR classifiers often select a scattered collection of independent features. We applied the method to both simulation data and real MEG data. We found that SSLR consistently increases classification accuracy, and produces weight vectors that are more meaningful from a neuroscientific perspective. (c) 2012 Elsevier Inc. All rights reserved.

Web of Science ® 被引用回数 : 15

リンク情報
DOI
https://doi.org/10.1016/j.neuroimage.2011.12.085
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000303272300065&DestApp=WOS_CPL
ID情報
  • DOI : 10.1016/j.neuroimage.2011.12.085
  • ISSN : 1053-8119
  • eISSN : 1095-9572
  • Web of Science ID : WOS:000303272300065

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