The use of unlabeled data has lead to an improvement in classification accuracy for a variety of classification problems via co-training approaches. In the co-training approach, the data has to be available in a dual view representation or two distinct classifiers are required. In this paper, a unified energy equation for classification combining labeled data and unlabeled data is introduced. This classification formulation is posed as a constrained minimum cut problem integrating labeling information on labeled data and cluster similarity information on unlabeled data for joint estimation. A novel constrained randomized contraction algorithm is proposed for finding the solution to the constrained minimum cuts problem. Experimental results on standard datasets and synthetic datasets are presented.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Constrained minimum cut for classification using labeled and unlabeled data


    Beteiligte:
    Li, C.H. (Autor:in)


    Erscheinungsdatum :

    2001-01-01


    Format / Umfang :

    557914 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Constrained Minimum Cut for Classification Using Labeled and Unlabeled Data

    Li, C. H. / IEEE | British Library Conference Proceedings | 2001


    Learning Bayesian Network Classifiers for Facial Expression Recognition Using both Labeled and Unlabeled Data

    Cohen, I. / Sebe, N. / Cozman, F. et al. | British Library Conference Proceedings | 2003



    An Efficient Labeled/Unlabeled Random Finite Set Algorithm for Multiobject Tracking

    Kropfreiter, Thomas / Meyer, Florian / Hlawatsch, Franz | IEEE | 2022