In the era of big data, data may come from multiple sources, known as
multi-view data. Multi-view clustering aims at generating better clusters by
exploiting complementary and consistent information from multiple views rather
than relying on the individual view...
Due to inevitable system errors caused by
data-captured sensors or others, the data in each view may be erroneous. Various types of errors behave differently and inconsistently in each view. More precisely, error could exhibit as noise and corruptions in reality. Unfortunately, none of the existing multi-view clustering approaches handle all
of these error types. Consequently, their clustering performance is
dramatically degraded. In this paper, we propose a novel Markov chain method
for Error-Robust Multi-View Clustering (EMVC). By decomposing each view into a
shared transition probability matrix and error matrix and imposing structured
sparsity-inducing norms on error matrices, we characterize and handle typical
types of errors explicitly. To solve the challenging optimization problem, we
propose a new efficient algorithm based on Augmented Lagrangian Multipliers and
prove its convergence rigorously. Experimental results on various synthetic and
real-world datasets show the superiority of the proposed EMVC method over the
baseline methods and its robustness against different types of errors.