Multi-dimensional clustering through fusion of high-order similarities

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Clustering objects with heterogeneous attributes captured from different dimensions remains challenging in integrating the multiple dimensional information. Most of the current multi-dimensional clustering models pin on direct sample-wised similarity and fail to exploit hidden mutual affinity among different sampling spaces. Thus, it is hard to capture a legible cluster structure. To tackle this issue, we propose a High-order multi-dimensional Spectral Clustering method (HSC). The proposed HSC aims to learn a high-order similarity to characterize the intrinsic relationship among different dimensional spaces instead of the ordinary similarity. It then performs a clustering task within a latent space by jointly learning the high-order similarity and ordinary similarity. Extensive experiments over synthetic and real-world data sets show that the proposed HSC outperforms benchmark multi-dimensional methods in most scenarios and is capable of revealing a reliable structure concealed across multi-dimensional spaces.