Complexity versus agreement for many views Co-regularization for multi-view semi-supervised learning
نویسندگان
چکیده
The paper considers the problem of semi-supervised multiview classification, where each view corresponds to a Reproducing Kernel Hilbert Space. An algorithm based on co-regularization methods with extra penalty terms reflecting smoothness and general agreement properties is proposed. We first provide explicit tight control on the Rademacher (L1) complexity of the corresponding class of learners for arbitrary many views, then give the asymptotic behavior of the bounds when the coregularization term increases, making explicit the relation between consistency of the views and reduction of the search space. Since many views involve many parameters, we third provide a parameter selection procedure, based on the stability approach with clustering and localization arguments. To this aim, we give an explicit bound on the variance (L2diameter) of the class of functions. Finally we illustrate the algorithm through simulations on toy examples.
منابع مشابه
A Co-Regularization Approach to Semi-supervised Learning with Multiple Views
The Co-Training algorithm uses unlabeled examples in multiple views to bootstrap classifiers in each view, typically in a greedy manner, and operating under assumptions of view-independence and compatibility. In this paper, we propose a Co-Regularization framework where classifiers are learnt in each view through forms of multi-view regularization. We propose algorithms within this framework th...
متن کاملA Survey on Multi-View Clustering
With advances in information acquisition technologies, multi-view data become ubiquitous. Multi-view learning has thus become more and more popular in machine learning and data mining fields. Multi-view unsupervised or semi-supervised learning, such as co-training, co-regularization has gained considerable attention. Although recently, multi-view clustering (MVC) methods have been developed rap...
متن کاملThe Rademacher Complexity of Co-Regularized Kernel Classes
In the multi-view approach to semisupervised learning, we choose one predictor from each of multiple hypothesis classes, and we co-regularize our choices by penalizing disagreement among the predictors on the unlabeled data. We examine the co-regularization method used in the coregularized least squares (CoRLS) algorithm [12], in which the views are reproducing kernel Hilbert spaces (RKHS's), a...
متن کاملLearning Preferences with Co-Regularized Least-Squares
Situations when only a limited amount of labeled data and a large amount of unlabeled data is available to the learning algorithm are typical for many real-world problems. In this paper, we propose a semi-supervised preference learning algorithm that is based on the multiview approach. Multi-view learning algorithms operate by constructing a predictor for each view and by choosing such predicti...
متن کاملMulti-view Laplacian Support Vector Machines
We propose a new approach, multi-view Laplacian support vector machines (SVMs), for semi-supervised learning under the multiview scenario. It integrates manifold regularization and multi-view regularization into the usual formulation of SVMs and is a natural extension of SVMs from supervised learning to multi-view semi-supervised learning. The function optimization problem in a reproducing kern...
متن کامل