Abstract: In recent years, multiple-kernel clustering (MKC) has achieved remarkable progress in fusing information from multi-source to boost the performance of clustering. However, denoting n as the sample number, the O(n2)  memory consumption and the  O(n3) computational consumption limit the practicality of these methods. In this paper, we redesign the formulation of subspace segmentation-based MKC, thereby reducing its memory and computational complexity to O(n)  and  O(n2), respectively. In the proposed algorithm, maned Compressed Subspace Alignment based Multiple Kernel Clustering(CSA-MKC), we sample only a part of the data to reconstruct the whole dataset. Specifically, in our design, a consensus sampling matrix is learned simultaneously with the information fusion process, so as to make the generated anchor point set more suitable for data reconstruction across different views. Consequently, the discriminative capability of the reconstruction matrix is improved, and the performance of clustering is enhanced. Moreover, since our algorithm is straightforward for parallelization, through the acceleration of GPU, our algorithm can achieve superior performance against the compared state-of-the-art methods on six datasets with square time cost. 

Key words: multiple-kernel clustering, subspace clustering, subspace alignment, multi-view cluster- ing, large-scale machine learning