Automatic Model Selection in Subspace Clustering via Triplet Relationships

Jufeng Yang, Jie Liang, Kai Wang, Yongliang Yang, Ming-Ming Cheng

Research output: Contribution to conferencePaperpeer-review

6 Citations (SciVal)


This paper addresses both the model selection (i.e. estimating the number of clusters K ) and subspace clustering problems in a unified model. The real data always distribute on a union of low-dimensional sub-manifolds which are embedded in a high-dimensional ambient space. In this regard, the state-ofthe-art subspace clustering approaches firstly learn the affinity among samples, followed by a spectral clustering to generate the segmentation. However, arguably, the intrinsic geometrical structures among samples are rarely considered in the optimization process. In this paper, we propose to simultaneously estimate K and segment the samples according to the local similarity relationships derived from the affinity matrix. Given the correlations among samples, we define a novel data structure termed the Triplet, each of which reflects a high relevance and locality among three samples which are aimed to be segmented into the same subspace. While the traditional pairwise distance can be close between inter-cluster samples lying on the intersection of two subspaces, the wrong assignments can be avoided by the hyper-correlation derived from the proposed triplets due to the complementarity of multiple constraints. Sequentially, we propose to greedily optimize a new model selection reward to estimate K according to the correlations between inter-cluster triplets. We simultaneously optimize a fusion reward based on the similarities between triplets and clusters to generate the final segmentation. Extensive experiments on the benchmark datasets demonstrate the effectiveness and robustness of the proposed approach.
Original languageEnglish
Number of pages8
Publication statusPublished - 2018
EventAAAI Conference on Artificial Intelligence 2018 -
Duration: 2 Feb 20187 Feb 2018


ConferenceAAAI Conference on Artificial Intelligence 2018


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