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Pseudo labels approach to interpretable self-guided subspace clustering

Year: 2025
Type: article
Abstract: Majority subspace clustering (SC) algorithms depend on one or more hyperparameters that need to be tuned for the SC algorithms to achieve high clustering performance. This is often performed using grid-search, assuming that held out set is available. In some domains, such as medicine, this assumption does not hold true in many cases. To address this problem, we propose an approach to label independent hyperparameter optimization using clustering quality metrics, such as accuracy (ACC) or normalized mutual information (NMI), that are computed based on pseudo labels obtained from the SC algorithm itself across a predefined grid of hyperparameters. Assuming that ACC (or NMI) is a smooth function of hyperparameter values it is possible to select subintervals of hyperparameters, which are then iteratively further split into halves or thirds until a relative error criterion is satisfied. In principle, the hyperparameters of any SC algorithm can be tuned using the proposed method. We demonstrate this approach on five single-view SC algorithms and two multi-view SC algorithms, comparing the achieved performance with their oracle versions across six datasets representing digits, faces and objects. The proposed method typically achieves clustering performance that is up to 7% lower than that of the oracle versions. We also make proposed method interpretable by visualizing subspace bases, which are estimated from the computed clustering partitions. This aids in the initial selection of the hyperparameter search space.
DOI: 10.1016/j.patcog.2025.112618
Language: English
Open Access status: hybrid
Estimated APC paid: $2,590
OpenAlex ID: W4415196679
FWCI: 0.411
Cites: 44
Cited by: 1
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