Semi-supervised adaptive kernel concept factorization

Kernelized concept factorization (KCF) has shown its advantage on handling data with nonlinear structures; however, the kernels involved in the existing KCF-based methods are empirically predefined, which may compromise the performance. In this paper, we propose semi-supervised adaptive kernel concept factorization (SAKCF), which integrates the data representation and kernel learning into a unified model to make the two learning processes adapt to each other. SAKCF extends traditional KCF in a semi-supervised manner, which encourages the high-dimensional representation to be consistent with both the limited supervisory and local geometric information. Besides, an alternating iterative algorithm is proposed to solve the resulting constrained optimization problem. Experimental results on six real-world data sets verify the effectiveness and advantages of our SAKCF over state-of-the-art methods when applied on the clustering task.