  With recent advances in data collection from multiple sources, multi-view
data has received significant attention. In multi-view data, each view
represents a different perspective of data. Since label information is often
expensive to acquire, multi-view clustering has gained growing interest, which
aims to obtain better clustering solution by exploiting complementary and
consistent information across all views rather than only using an individual
view. Due to inevitable sensor failures, data in each view may contain error.
Error often exhibits as noise or feature-specific corruptions or outliers.
Multi-view data may contain any or combination of these error types. Blindly
clustering multi-view data i.e., without considering possible error in view(s)
could significantly degrade the performance. The goal of error-robust
multi-view clustering is to obtain useful outcome even if the multi-view data
is corrupted. Existing error-robust multi-view clustering approaches with
explicit error removal formulation can be structured into five broad research
categories - sparsity norm based approaches, graph based methods, subspace
based learning approaches, deep learning based methods and hybrid approaches,
this survey summarizes and reviews recent advances in error-robust clustering
for multi-view data. Finally, we highlight the challenges and provide future
research opportunities.
