Semi-Supervised Learning Using Kernel Spectral Clustering Core Model
نویسندگان
چکیده
A multi-class semi-supervised learning algorithm formulated as a regularized kernel spectral clustering (KSC) approach is proposed. The method is bale to address both semi-supervised classification and clustering. In addition a low embedding dimension is utilized to reveal the existing number of clusters. Thanks to the Nyström approximation technique, the approach can be scaled up for analyzing large scale datasets. The model is trained using a training dataset and the class/cluster membership of the test data points are estimated using the out-of-sample extension property of the model.
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تاریخ انتشار 2015