A novel multiple kernel learning approach for semi-supervised clustering

Distance metrics are widely used in various machine learning and pattern recognition algorithms. A main issue in these algorithms is choosing the proper distance metric. In recent years, learning an appropriate distance metric has become a very active research field. In the kernelised version of distance metric learning algorithms, the data points are implicitly mapped into a higher dimensional feature space and the learning process is performed in the resulted feature space. The performance of the kernel-based methods heavily depends on the chosen kernel function. So, selecting an appropriate kernel function and/or tuning its parameter(s) impose significant challenges in such methods. The Multiple Kernel Learning theory (MKL) addresses this problem by learning a linear combination of a number of predefined kernels. In this paper, we formulate the MKL problem in a semi-supervised metric learning framework. In the proposed approach, pairwise similarity constraints are used to adjust the weights of the combined kernels and simultaneously learn the appropriate distance metric. Using both synthetic and real-world datasets, we show that the proposed method outperforms some recently introduced semi-supervised metric learning approaches.