  Subspace clustering methods which embrace a self-expressive model that
represents each data point as a linear combination of other data points in the
dataset provide powerful unsupervised learning techniques. However, when
dealing with large datasets, representation of each data point by referring to
all data points via a dictionary suffers from high computational complexity. To
alleviate this issue, we introduce a parallelizable multi-subset based
self-expressive model (PMS) which represents each data point by combining
multiple subsets, with each consisting of only a small proportion of the
samples. The adoption of PMS in subspace clustering (PMSSC) leads to
computational advantages because the optimization problems decomposed over each
subset are small, and can be solved efficiently in parallel. Furthermore, PMSSC
is able to combine multiple self-expressive coefficient vectors obtained from
subsets, which contributes to an improvement in self-expressiveness. Extensive
experiments on synthetic and real-world datasets show the efficiency and
effectiveness of our approach in comparison to other methods.
