  Real data often appear in the form of multiple incomplete views. Incomplete
multi-view clustering is an effective method to integrate these incomplete
views. Previous methods only learn the consistent information between different
views and ignore the unique information of each view, which limits their
clustering performance and generalizations. To overcome this limitation, we
propose a novel View Variation and View Heredity approach (V3H). Inspired by
the variation and the heredity in genetics, V3H first decomposes each subspace
into a variation matrix for the corresponding view and a heredity matrix for
all the views to represent the unique information and the consistent
information respectively. Then, by aligning different views based on their
cluster indicator matrices, V3H integrates the unique information from
different views to improve the clustering performance. Finally, with the help
of the adjustable low-rank representation based on the heredity matrix, V3H
recovers the underlying true data structure to reduce the influence of the
large incompleteness. More importantly, V3H presents possibly the first work to
introduce genetics to clustering algorithms for learning simultaneously the
consistent information and the unique information from incomplete multi-view
data. Extensive experimental results on fifteen benchmark datasets validate its
superiority over other state-of-the-arts.
