Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99661
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dc.contributor.authorDuan, Lixinen
dc.contributor.authorTsang, Ivor Wai-Hungen
dc.contributor.authorXu, Dongen
dc.contributor.authorLi, Wenen
dc.date.accessioned2013-08-06T03:14:44Zen
dc.date.accessioned2019-12-06T20:09:58Z-
dc.date.available2013-08-06T03:14:44Zen
dc.date.available2019-12-06T20:09:58Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.urihttps://hdl.handle.net/10356/99661-
dc.identifier.urihttp://hdl.handle.net/10220/13032en
dc.description.abstractWe propose a multi-view learning approach called co-labeling which is applicable for several machine learning problems where the labels of training samples are uncertain, including semi-supervised learning (SSL), multi-instance learning (MIL) and max-margin clustering (MMC). Particularly, we first unify those problems into a general ambiguous problem in which we simultaneously learn a robust classifier as well as find the optimal training labels from a finite label candidate set. To effectively utilize multiple views of data, we then develop our co-labeling approach for the general multi-view ambiguous problem. In our work, classifiers trained on different views can teach each other by iteratively passing the predictions of training samples from one classifier to the others. The predictions from one classifier are considered as label candidates for the other classifiers. To train a classifier with a label candidate set for each view, we adopt the Multiple Kernel Learning (MKL) technique by constructing the base kernel through associating the input kernel calculated from input features with one label candidate. Compared with the traditional co-training method which was specifically designed for SSL, the advantages of our co-labeling are two-fold: 1) it can be applied to other ambiguous problems such as MIL and MMC, 2) it is more robust by using the MKL method to integrate multiple labeling candidates obtained from different iterations and biases. Promising results on several real-world multi-view data sets clearly demonstrate the effectiveness of our proposed co-labeling for both MIL and SSL.en
dc.language.isoenen
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleCo-labeling : a new multi-view learning approach for ambiguous problemsen
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Engineeringen
dc.contributor.conferenceIEEE International Conference on Data Mining (12th : 2012 : Brussels, Belgium)en
dc.identifier.doihttp://dx.doi.org/10.1109/ICDM.2012.78en
item.grantfulltextnone-
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