  Incomplete multi-view clustering, which aims to solve the clustering problem
on the incomplete multi-view data with partial view missing, has received more
and more attention in recent years. Although numerous methods have been
developed, most of the methods either cannot flexibly handle the incomplete
multi-view data with arbitrary missing views or do not consider the negative
factor of information imbalance among views. Moreover, some methods do not
fully explore the local structure of all incomplete views. To tackle these
problems, this paper proposes a simple but effective method, named localized
sparse incomplete multi-view clustering (LSIMVC). Different from the existing
methods, LSIMVC intends to learn a sparse and structured consensus latent
representation from the incomplete multi-view data by optimizing a sparse
regularized and novel graph embedded multi-view matrix factorization model.
Specifically, in such a novel model based on the matrix factorization, a l1
norm based sparse constraint is introduced to obtain the sparse low-dimensional
individual representations and the sparse consensus representation. Moreover, a
novel local graph embedding term is introduced to learn the structured
consensus representation. Different from the existing works, our local graph
embedding term aggregates the graph embedding task and consensus representation
learning task into a concise term. Furthermore, to reduce the imbalance factor
of incomplete multi-view learning, an adaptive weighted learning scheme is
introduced to LSIMVC. Finally, an efficient optimization strategy is given to
solve the optimization problem of our proposed model. Comprehensive
experimental results performed on six incomplete multi-view databases verify
that the performance of our LSIMVC is superior to the state-of-the-art IMC
approaches. The code is available in https://github.com/justsmart/LSIMVC.
