Incomplete Multi-Kernel k-Means Clustering With Fractional-Order Embedding

Multiple kernel clustering (MKC) has received increasing attention in the community of machine learning, which takes advantage of multiple pre-specified kernels to perform clustering tasks. Traditional MKC algorithms cannot effectively deal with the incomplete views where some samples are missing. Thus, incomplete MKC (IMKC) has been developed to solve this problem and obtained promising results. Nevertheless, the samples may be noisy or limited in real-world applications, which will result in the performance deterioration of existing IMKC algorithms. To address this issue, in this paper we propose a simple yet effective clustering method for incomplete data, termed fractional-order embedding incomplete multi-kernel k-means clustering (FE-MKKM-IK). Specifically, FE-MKKM-IK introduces the idea of fractional-order embedding to reconstruct the kernel matrix computed by the samples. On this basis, a new incomplete multiple kernel k-means clustering is developed. Performance evaluation is conducted on four widely used datasets, which shows that FE-MKKM-IK is effective to cluster the incomplete data.