A Spectral Kernel Learning Algorithm for Classification

Semi-supervised kernel learning is an important technique for classification and has been actively studied recently. In this paper, we propose a new semi-supervised spectral kernel learning method to learn a new kernel matrix with both labeled data and unlabeled data, which tunes the spectral of a standard kernel matrix by maximizing the margin between two classes. Our approach can be turned into a non-linear optimization problem. We use lagrangian support vector machines and gradient descent algorithm together to solve our optimization problem efficiently. Experimental results show that our spectral kernel learning method is more effective for classification than traditional approaches.