@book{
 title = {Automatic motion segmentation via a cumulative kernel representation and spectral clustering},
 type = {book},
 year = {2017},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 keywords = {Kernel spectral clustering,Motion segmentation,Time-varying data,Variable ranking},
 volume = {10585 LNCS},
 id = {d0cfbeee-9083-3d25-a9e2-525a89e04f11},
 created = {2018-11-21T20:30:20.483Z},
 file_attached = {false},
 profile_id = {97193e8c-2867-3b74-b62f-1c69932d41b7},
 last_modified = {2025-02-28T20:56:52.935Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Ona-Rocha2017},
 folder_uuids = {c39d0ad9-5f0e-4372-9973-501bb75dac98},
 private_publication = {false},
 abstract = {© Springer International Publishing AG 2017. Dynamic or time-varying data analysis is of great interest in emerging and challenging research on automation and machine learning topics. In particular, motion segmentation is a key stage in the design of dynamic data analysis systems. Despite several studies have addressed this issue, there still does not exist a final solution highly compatible with subsequent clustering/classification tasks. In this work, we propose a motion segmentation compatible with kernel spectral clustering (KSC), here termed KSC-MS, which is based on multiple kernel learning and variable ranking approaches. Proposed KSC-MS is able to automatically segment movements within a dynamic framework while providing robustness to noisy environments.},
 bibtype = {book},
 author = {Oña-Rocha, O.R. and Sánchez-Manosalvas, O.T. and Umaquinga-Criollo, A.C. and Rosero-Montalvo, P.D. and Suárez-Zambrano, L.E. and Rodríguez-Sotelo, J.L. and Peluffo-Ordóñez, D.H.},
 doi = {10.1007/978-3-319-68935-7_44}
}

{"_id":"KSn2zGpSt4LJmKjWW","bibbaseid":"oarocha-snchezmanosalvas-umaquingacriollo-roseromontalvo-surezzambrano-rodrguezsotelo-peluffoordez-automaticmotionsegmentationviaacumulativekernelrepresentationandspectralclustering-2017","authorIDs":["5e473f4cd8f94bde0100001b","5e474350d8f94bde01000053","5e4748edd8f94bde01000095","5e47627f45a735de0100005c","5e47896527a0c8de010000d7","5e4887d586dc17de010000bd","5e489f536ac344de010000d2","5e49de91b63120f201000112","5e4a14cccc11a8df01000137","5e4b5d5bcf8e89df0100003a","5e4c6cb6f2c6ddde010000a7","5e4c8af55cc521f20100006d","5e4dff4bcc196bde01000139","5e4ea65364b624de010000e7","5e4ee2c12962cadf0100014a","5e4f76dca01931de010000cf","5e50810a7e0827df0100004e","5e52108dbba759e801000075","5e5258dd2eb8f4de0100001e","5e55ef91c2c8a2df010000c2","5e59f31c6ef27ddf01000058","AWdnrMsEAL4dCtwaa","YNcYvua5iyqmYjaBk"],"author_short":["Oña-Rocha, O.","Sánchez-Manosalvas, O.","Umaquinga-Criollo, A.","Rosero-Montalvo, P.","Suárez-Zambrano, L.","Rodríguez-Sotelo, J.","Peluffo-Ordóñez, D."],"bibdata":{"title":"Automatic motion segmentation via a cumulative kernel representation and spectral clustering","type":"book","year":"2017","source":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","keywords":"Kernel spectral clustering,Motion segmentation,Time-varying data,Variable ranking","volume":"10585 LNCS","id":"d0cfbeee-9083-3d25-a9e2-525a89e04f11","created":"2018-11-21T20:30:20.483Z","file_attached":false,"profile_id":"97193e8c-2867-3b74-b62f-1c69932d41b7","last_modified":"2025-02-28T20:56:52.935Z","read":false,"starred":false,"authored":"true","confirmed":"true","hidden":false,"citation_key":"Ona-Rocha2017","folder_uuids":"c39d0ad9-5f0e-4372-9973-501bb75dac98","private_publication":false,"abstract":"© Springer International Publishing AG 2017. Dynamic or time-varying data analysis is of great interest in emerging and challenging research on automation and machine learning topics. In particular, motion segmentation is a key stage in the design of dynamic data analysis systems. Despite several studies have addressed this issue, there still does not exist a final solution highly compatible with subsequent clustering/classification tasks. In this work, we propose a motion segmentation compatible with kernel spectral clustering (KSC), here termed KSC-MS, which is based on multiple kernel learning and variable ranking approaches. Proposed KSC-MS is able to automatically segment movements within a dynamic framework while providing robustness to noisy environments.","bibtype":"book","author":"Oña-Rocha, O.R. and Sánchez-Manosalvas, O.T. and Umaquinga-Criollo, A.C. and Rosero-Montalvo, P.D. and Suárez-Zambrano, L.E. and Rodríguez-Sotelo, J.L. and Peluffo-Ordóñez, D.H.","doi":"10.1007/978-3-319-68935-7_44","bibtex":"@book{\n title = {Automatic motion segmentation via a cumulative kernel representation and spectral clustering},\n type = {book},\n year = {2017},\n source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},\n keywords = {Kernel spectral clustering,Motion segmentation,Time-varying data,Variable ranking},\n volume = {10585 LNCS},\n id = {d0cfbeee-9083-3d25-a9e2-525a89e04f11},\n created = {2018-11-21T20:30:20.483Z},\n file_attached = {false},\n profile_id = {97193e8c-2867-3b74-b62f-1c69932d41b7},\n last_modified = {2025-02-28T20:56:52.935Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Ona-Rocha2017},\n folder_uuids = {c39d0ad9-5f0e-4372-9973-501bb75dac98},\n private_publication = {false},\n abstract = {© Springer International Publishing AG 2017. Dynamic or time-varying data analysis is of great interest in emerging and challenging research on automation and machine learning topics. In particular, motion segmentation is a key stage in the design of dynamic data analysis systems. Despite several studies have addressed this issue, there still does not exist a final solution highly compatible with subsequent clustering/classification tasks. In this work, we propose a motion segmentation compatible with kernel spectral clustering (KSC), here termed KSC-MS, which is based on multiple kernel learning and variable ranking approaches. Proposed KSC-MS is able to automatically segment movements within a dynamic framework while providing robustness to noisy environments.},\n bibtype = {book},\n author = {Oña-Rocha, O.R. and Sánchez-Manosalvas, O.T. and Umaquinga-Criollo, A.C. and Rosero-Montalvo, P.D. and Suárez-Zambrano, L.E. and Rodríguez-Sotelo, J.L. and Peluffo-Ordóñez, D.H.},\n doi = {10.1007/978-3-319-68935-7_44}\n}","author_short":["Oña-Rocha, O.","Sánchez-Manosalvas, O.","Umaquinga-Criollo, A.","Rosero-Montalvo, P.","Suárez-Zambrano, L.","Rodríguez-Sotelo, J.","Peluffo-Ordóñez, D."],"biburl":"https://bibbase.org/service/mendeley/97193e8c-2867-3b74-b62f-1c69932d41b7","bibbaseid":"oarocha-snchezmanosalvas-umaquingacriollo-roseromontalvo-surezzambrano-rodrguezsotelo-peluffoordez-automaticmotionsegmentationviaacumulativekernelrepresentationandspectralclustering-2017","role":"author","urls":{},"keyword":["Kernel spectral clustering","Motion segmentation","Time-varying data","Variable ranking"],"metadata":{"authorlinks":{}},"downloads":1},"bibtype":"book","biburl":"https://bibbase.org/service/mendeley/97193e8c-2867-3b74-b62f-1c69932d41b7","creationDate":"2020-02-15T00:33:47.597Z","downloads":1,"keywords":["kernel spectral clustering","motion segmentation","time-varying data","variable ranking"],"search_terms":["automatic","motion","segmentation","via","cumulative","kernel","representation","spectral","clustering","oña-rocha","sánchez-manosalvas","umaquinga-criollo","rosero-montalvo","suárez-zambrano","rodríguez-sotelo","peluffo-ordóñez"],"title":"Automatic motion segmentation via a cumulative kernel representation and spectral clustering","year":2017,"dataSources":["ntXyXv2964fDt3myF","ya2CyA73rpZseyrZ8","bf4Wvyy9Na82zPs7u","mL2WrmsYpxN4FsHKg","2252seNhipfTmjEBQ"]}