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dc.contributor.authorMartín Muñoz, Mario
dc.contributor.authorBéjar Alonso, Javier
dc.contributor.authorEspósito, Gennaro
dc.contributor.authorCatala Roig, Neus
dc.contributor.authorCortés García, Claudio Ulises
dc.contributor.authorViñas, Ferran
dc.contributor.authorTarragó Bofarull, Josep
dc.contributor.authorRojo, Emilio
dc.contributor.authorNowak, Rafal
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.identifier.citationMartin, M., Bejar, J., Espósito, G., Català Roig, N., Cortes, C., Viñas, F., Tarragó, J., Rojo, E., Nowak, R. Kernel alignment for identifying objective criteria from brain MEG recordings in schizophrenia. "Pattern recognition letters", 2 Agost 2016.
dc.description.abstractThe current wide access to data from different neuroimaging techniques has permitted to obtain data to explore the possibility of finding objective criteria that can be used for diagnostic purposes. In order to decide which features of the data are relevant for the diagnostic task, we present in this paper a simple method for feature selection based on kernel alignment with the ideal kernel in support vector machines (SVM). The method presented shows state-of-the-art performance while being more efficient than other methods for feature selection in SVM. It is also less prone to overfitting due to the properties of the alignment measure. All these abilities are essential in neuroimaging study, where the number of features representing recordings is usually very large compared with the number of recordings. The method has been applied to a dataset in order to determine objective criteria for the diagnosis of schizophrenia. The dataset analyzed has been obtained from multichannel magnetoencephalogram (MEG) recordings, corresponding to the recordings during the performance of a mismatch negativity (MMN) auditory task by a set of schizophrenia patients and a control group. All signal frequency bands are analyzed (from d (1–4 Hz) to high frequency ¿ (60–200 Hz)) and the signal correlations among the different sensors for these frequencies are used as features.
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
dc.subjectÀrees temàtiques de la UPC::Ciències de la salut::Salut mental
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.lcshKernel functions
dc.subject.lcshSupport vector machines
dc.subject.otherMachine learning
dc.subject.otherFeature selection
dc.subject.otherKernel methods
dc.titleKernel alignment for identifying objective criteria from brain MEG recordings in schizophrenia
dc.subject.lemacCervell -- Processament de dades
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacKernel, Funcions de
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorMartin, M.; Bejar, J.; Espósito, G.; Català Roig, N.; Cortes, C.; Viñas, F.; Tarragó, J.; Rojo, E.; Nowak, R.
upcommons.citation.publicationNamePattern recognition letters

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