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The performance of most the clustering methods hinges on the used pairwiseaffinity, which is usually denoted by a similarity matrix. However, thepairwise similarity is notoriously known for its vulnerability of noisecontamination or the imbalance in samples or features, and thus hindersaccurate clustering. To tackle this issue, we propose to use information amongsamples to boost the clustering performance. We proved that a simplifiedsimilarity for pairs, denoted by a fourth order tensor, equals to the Kroneckerproduct of pairwise similarity matrices under decomposable assumption, orprovide complementary information for which the pairwise similarity missedunder indecomposable assumption. Then a high order similarity matrix isobtained from the tensor similarity via eigenvalue decomposition. The highorder similarity capturing spatial information serves as a robust complementfor the pairwise similarity. It is further integrated with the popular pairwisesimilarity, named by IPS2, to boost the clustering performance. Extensiveexperiments demonstrated that the proposed IPS2 significantly outperformedprevious similarity-based methods on real-world datasets and it was capable ofhandling the clustering task over under-sampled and noisy datasets.

Integrating Tensor Similarity to Enhance Clustering Performance