Unsupervised Absent Multiple Kernel Extreme Learning Machine.

Learning from the absent multiple sources data is very challenging, especially when lacking label information. Although existing multiple kernel learning methods with kernel imputing achieved remarkable performance, they may fail in practical issues due to hardly tracking the data with huge missing samples or easily fall into a local optimum. Thus, we propose an unsupervised absent multiple kernel extreme learning machine to effectively deal with absent data. It firstly extracts information from absent multiple sources data via multiple kernels to structure a new data space named K-space. Then, the optimal multiple kernel combination coefficients are learned in the K-space. Finally, we establish a seamlessly integrated object function to complete the absent kernel matrices and acquire the clustering information simultaneously by a well-designed optimization strategy within finite steps. As evidenced by comprehensive experiments, the proposed method provides significantly better clustering performance.