  Clustering is a fundamental task in the computer vision and machine learning
community. Although various methods have been proposed, the performance of
existing approaches drops dramatically when handling incomplete
high-dimensional data (which is common in real world applications). To solve
the problem, we propose a novel deep incomplete clustering method, named Deep
Distribution-preserving Incomplete Clustering with Optimal Transport (DDIC-OT).
To avoid insufficient sample utilization in existing methods limited by few
fully-observed samples, we propose to measure distribution distance with the
optimal transport for reconstruction evaluation instead of traditional
pixel-wise loss function. Moreover, the clustering loss of the latent feature
is introduced to regularize the embedding with more discrimination capability.
As a consequence, the network becomes more robust against missing features and
the unified framework which combines clustering and sample imputation enables
the two procedures to negotiate to better serve for each other. Extensive
experiments demonstrate that the proposed network achieves superior and stable
clustering performance improvement against existing state-of-the-art incomplete
clustering methods over different missing ratios.
