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Subspace clustering methods which embrace a self-expressive model thatrepresents each data point as a linear combination of other data points in thedataset provide powerful unsupervised learning techniques. However, whendealing with large datasets, representation of each data point by referring toall data points via a dictionary suffers from high computational complexity. Toalleviate this issue, we introduce a parallelizable multi-subset basedself-expressive model (PMS) which represents each data point by combiningmultiple subsets, with each consisting of only a small proportion of thesamples. The adoption of PMS in subspace clustering (PMSSC) leads tocomputational advantages because the optimization problems decomposed over eachsubset are small, and can be solved efficiently in parallel. Furthermore, PMSSCis able to combine multiple self-expressive coefficient vectors obtained fromsubsets, which contributes to an improvement in self-expressiveness. Extensiveexperiments on synthetic and real-world datasets show the efficiency andeffectiveness of our approach in comparison to other methods.

PMSSC: Parallelizable multi-subset based self-expressive model for subspace clustering