Linked Life Data

21761690

Source:http://linkedlifedata.com/resource/pubmed/id/21761690

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2011-7-18
pubmed:abstractText
Many current medical image analysis problems involve learning thousands or even millions of model parameters from extremely few samples. Employing sparse models provides an effective means for handling the curse of dimensionality, but other propitious properties beyond sparsity are typically not modeled. In this paper, we propose a simple approach, generalized sparse regularization (GSR), for incorporating domain-specific knowledge into a wide range of sparse linear models, such as the LASSO and group LASSO regression models. We demonstrate the power of GSR by building anatomically-informed sparse classifiers that additionally model the intrinsic spatiotemporal characteristics of brain activity for fMRI classification. We validate on real data and show how prior-informed sparse classifiers outperform standard classifiers, such as SVM and a number of sparse linear classifiers, both in terms of prediction accuracy and result interpretability. Our results illustrate the added-value in facilitating flexible integration of prior knowledge beyond sparsity in large-scale model learning problems.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1011-2499
pubmed:author
pubmed:issnType
Print
pubmed:volume
22
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
612-23
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Generalized sparse regularization with application to fMRI brain decoding.
pubmed:affiliation
Biomedical Signal and Image Computing Lab, UBC, Canada. bernardyng@gmail.com
pubmed:publicationType
Journal Article