@inbook{
 type = {inbook},
 year = {2015},
 keywords = {Dimensionality reduction,Generalized kernel,Kernel PCA,Multiple kernel learning},
 pages = {626-634},
 websites = {http://link.springer.com/10.1007/978-3-319-25751-8_75},
 id = {5b86c0c1-e950-36a4-a153-cfc3cf1c983d},
 created = {2022-01-26T03:01:09.607Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {b9022d50-068c-31b4-9174-ebfaaf9ee57b},
 last_modified = {2022-01-26T03:01:09.607Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Peluffo-Ordonez2015c},
 private_publication = {false},
 abstract = {This work introduces a multiple kernel learning (MKL) approach for selecting and combining different spectralmethods of dimensionality reduction (DR).From a predefined set of kernels representing conventional spectralDRmethods, a generalized kernel is calculated by means of a linear combination of kernel matrices. Coefficients are estimated via a variable ranking aimed at quantifying how much each variable contributes to optimize a variance preservation criterion. All considered kernels are testedwithinakernelPCAframework.Theexperiments are carriedoutover well-known real and artificial data sets. The performance of compared DR approaches is quantified by a scaled version of the average agreement rate between K-ary neighborhoods. Proposed MKL approach exploits the representation ability of every single method to reach a better embedded data for both getting more intelligible visualization and preserving the structure of data.},
 bibtype = {inbook},
 author = {Peluffo-Ordóñez, Diego Hernán and Castro-Ospina, Andrés Eduardo and Alvarado-Pérez, Juan Carlos and Revelo-Fuelagán, Edgardo Javier},
 doi = {10.1007/978-3-319-25751-8_75},
 chapter = {Multiple Kernel Learning for Spectral Dimensionality Reduction},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

{"_id":"PqMR6CWhrRominNBo","bibbaseid":"peluffoordez-castroospina-alvaradoprez-revelofuelagn-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2015","authorIDs":[],"author_short":["Peluffo-Ordóñez, D., H.","Castro-Ospina, A., E.","Alvarado-Pérez, J., C.","Revelo-Fuelagán, E., J."],"bibdata":{"type":"inbook","year":"2015","keywords":"Dimensionality reduction,Generalized kernel,Kernel PCA,Multiple kernel learning","pages":"626-634","websites":"http://link.springer.com/10.1007/978-3-319-25751-8_75","id":"5b86c0c1-e950-36a4-a153-cfc3cf1c983d","created":"2022-01-26T03:01:09.607Z","file_attached":false,"profile_id":"aba9653c-d139-3f95-aad8-969c487ed2f3","group_id":"b9022d50-068c-31b4-9174-ebfaaf9ee57b","last_modified":"2022-01-26T03:01:09.607Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"Peluffo-Ordonez2015c","private_publication":false,"abstract":"This work introduces a multiple kernel learning (MKL) approach for selecting and combining different spectralmethods of dimensionality reduction (DR).From a predefined set of kernels representing conventional spectralDRmethods, a generalized kernel is calculated by means of a linear combination of kernel matrices. Coefficients are estimated via a variable ranking aimed at quantifying how much each variable contributes to optimize a variance preservation criterion. All considered kernels are testedwithinakernelPCAframework.Theexperiments are carriedoutover well-known real and artificial data sets. The performance of compared DR approaches is quantified by a scaled version of the average agreement rate between K-ary neighborhoods. Proposed MKL approach exploits the representation ability of every single method to reach a better embedded data for both getting more intelligible visualization and preserving the structure of data.","bibtype":"inbook","author":"Peluffo-Ordóñez, Diego Hernán and Castro-Ospina, Andrés Eduardo and Alvarado-Pérez, Juan Carlos and Revelo-Fuelagán, Edgardo Javier","doi":"10.1007/978-3-319-25751-8_75","chapter":"Multiple Kernel Learning for Spectral Dimensionality Reduction","title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","bibtex":"@inbook{\n type = {inbook},\n year = {2015},\n keywords = {Dimensionality reduction,Generalized kernel,Kernel PCA,Multiple kernel learning},\n pages = {626-634},\n websites = {http://link.springer.com/10.1007/978-3-319-25751-8_75},\n id = {5b86c0c1-e950-36a4-a153-cfc3cf1c983d},\n created = {2022-01-26T03:01:09.607Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {b9022d50-068c-31b4-9174-ebfaaf9ee57b},\n last_modified = {2022-01-26T03:01:09.607Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Peluffo-Ordonez2015c},\n private_publication = {false},\n abstract = {This work introduces a multiple kernel learning (MKL) approach for selecting and combining different spectralmethods of dimensionality reduction (DR).From a predefined set of kernels representing conventional spectralDRmethods, a generalized kernel is calculated by means of a linear combination of kernel matrices. Coefficients are estimated via a variable ranking aimed at quantifying how much each variable contributes to optimize a variance preservation criterion. All considered kernels are testedwithinakernelPCAframework.Theexperiments are carriedoutover well-known real and artificial data sets. The performance of compared DR approaches is quantified by a scaled version of the average agreement rate between K-ary neighborhoods. Proposed MKL approach exploits the representation ability of every single method to reach a better embedded data for both getting more intelligible visualization and preserving the structure of data.},\n bibtype = {inbook},\n author = {Peluffo-Ordóñez, Diego Hernán and Castro-Ospina, Andrés Eduardo and Alvarado-Pérez, Juan Carlos and Revelo-Fuelagán, Edgardo Javier},\n doi = {10.1007/978-3-319-25751-8_75},\n chapter = {Multiple Kernel Learning for Spectral Dimensionality Reduction},\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}","author_short":["Peluffo-Ordóñez, D., H.","Castro-Ospina, A., E.","Alvarado-Pérez, J., C.","Revelo-Fuelagán, E., J."],"urls":{"Website":"http://link.springer.com/10.1007/978-3-319-25751-8_75"},"biburl":"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3","bibbaseid":"peluffoordez-castroospina-alvaradoprez-revelofuelagn-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2015","role":"author","keyword":["Dimensionality reduction","Generalized kernel","Kernel PCA","Multiple kernel learning"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inbook","creationDate":"2020-12-30T00:49:12.160Z","downloads":0,"keywords":["dimensionality reduction","generalized kernel","kernel pca","multiple kernel learning"],"search_terms":["lecture","notes","computer","science","including","subseries","lecture","notes","artificial","intelligence","lecture","notes","bioinformatics","peluffo-ordóñez","castro-ospina","alvarado-pérez","revelo-fuelagán"],"title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","year":2015,"biburl":"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3","dataSources":["YEF3uFAbDNQXrkgNw","ya2CyA73rpZseyrZ8"]}