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dc.contributor.authorChen, George
dc.contributor.authorWachinger, Christian
dc.contributor.authorGolland, Polina
dc.date.accessioned2014-04-24T17:11:12Z
dc.date.available2014-04-24T17:11:12Z
dc.date.issued2013
dc.identifier.isbn978-3-642-38867-5
dc.identifier.isbn978-3-642-38868-2
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/1721.1/86232
dc.description.abstractManifold learning has been successfully applied to a variety of medical imaging problems. Its use in real-time applications requires fast projection onto the low-dimensional space. To this end, out-of-sample extensions are applied by constructing an interpolation function that maps from the input space to the low-dimensional manifold. Commonly used approaches such as the Nyström extension and kernel ridge regression require using all training points. We propose an interpolation function that only depends on a small subset of the input training data. Consequently, in the testing phase each new point only needs to be compared against a small number of input training data in order to project the point onto the low-dimensional space. We interpret our method as an out-of-sample extension that approximates kernel ridge regression. Our method involves solving a simple convex optimization problem and has the attractive property of guaranteeing an upper bound on the approximation error, which is crucial for medical applications. Tuning this error bound controls the sparsity of the resulting interpolation function. We illustrate our method in two clinical applications that require fast mapping of input images onto a low-dimensional space.en_US
dc.description.sponsorshipNational Alliance for Medical Image Computing (U.S.) (grant NIH NIBIB NAMIC U54-EB005149)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant NIH NCRR NAC P41-RR13218)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant NIH NIBIB NAC P41-EB-015902)en_US
dc.language.isoen_US
dc.publisherSpringer-Verlag Berlin Heidelbergen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/978-3-642-38868-2_25en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleSparse Projections of Medical Images onto Manifoldsen_US
dc.typeArticleen_US
dc.identifier.citationChen, George H., Christian Wachinger, and Polina Golland. “Sparse Projections of Medical Images onto Manifolds.” In Information Processing in Medical Imaging, Springer-Verlag (Lecture Notes in Computer Science; 7917) (2013): 292–303.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorChen, Georgeen_US
dc.contributor.mitauthorWachinger, Christianen_US
dc.contributor.mitauthorGolland, Polinaen_US
dc.relation.journalInformation Processing in Medical Imagingen_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsChen, George H.; Wachinger, Christian; Golland, Polinaen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-3652-1874
dc.identifier.orcidhttps://orcid.org/0000-0003-2516-731X
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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