{"data":{"id":"10.6084/m9.figshare.1606304","type":"dois","attributes":{"doi":"10.6084/m9.figshare.1606304","prefix":"10.6084","suffix":"m9.figshare.1606304","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Paynabar, Kamran","nameType":"Personal","givenName":"Kamran","familyName":"Paynabar","affiliation":[],"nameIdentifiers":[]},{"name":"Reed, Matthew P.","nameType":"Personal","givenName":"Matthew P.","familyName":"Reed","affiliation":[],"nameIdentifiers":[]},{"name":"Jin, Judy","nameType":"Personal","givenName":"Judy","familyName":"Jin","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Informative Sensor and Feature Selection via Hierarchical Nonnegative Garrote"}],"publisher":"Taylor \u0026 Francis","container":{},"publicationYear":2015,"subjects":[{"subject":"110309 Infectious Diseases","subjectScheme":"FOR"},{"subject":"FOS: Health sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Health sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Mathematics"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Mathematics","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Information and Computing Sciences"},{"subject":"Biological Sciences"},{"subject":"Chemistry"},{"subject":"Biotechnology"},{"subject":"Cell Biology"},{"subject":"Medicine"},{"subject":"Engineering"}],"contributors":[],"dates":[{"date":"2015-11-18","dateType":"Created"},{"date":"2015-11-18","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1080/00401706.2014.947383","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["6081 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Placing sensors in every station of a process or every element of a system to monistor its state or performance is usually too expensive or physically impossible. Therefore, a systematic method is needed to select important sensing variables. The method should not only be capable of identifying important sensors/signals among multistream signals from a distributed sensing system, but should also be able to extract a small set of interpretable features from the high-dimensional vector of a selected signal. For this purpose, we develop a new hierarchical regularization approach called hierarchical nonnegative garrote (NNG). At the first level of hierarchy, a group NNG is used to select important signals, and at the second level, the individual features within each signal are selected using a modified version of NNG that possesses good properties for the estimated coefficients. Performance of the proposed method is evaluated and compared with other existing methods through Monte Carlo simulation. A case study is conducted to demonstrate the proposed methodology that can be applied to develop a predictive model for the assessment of vehicle design comfort based on the tested drivers’ motion trajectory signals. 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