  Multi-view unsupervised feature selection has been proven to be efficient in
reducing the dimensionality of multi-view unlabeled data with high dimensions.
The previous methods assume all of the views are complete. However, in real
applications, the multi-view data are often incomplete, i.e., some views of
instances are missing, which will result in the failure of these methods.
Besides, while the data arrive in form of streams, these existing methods will
suffer the issues of high storage cost and expensive computation time. To
address these issues, we propose an Incremental Incomplete Multi-view
Unsupervised Feature Selection method (I$^2$MUFS) on incomplete multi-view
streaming data. By jointly considering the consistent and complementary
information across different views, I$^2$MUFS embeds the unsupervised feature
selection into an extended weighted non-negative matrix factorization model,
which can learn a consensus clustering indicator matrix and fuse different
latent feature matrices with adaptive view weights. Furthermore, we introduce
the incremental leaning mechanisms to develop an alternative iterative
algorithm, where the feature selection matrix is incrementally updated, rather
than recomputing on the entire updated data from scratch. A series of
experiments are conducted to verify the effectiveness of the proposed method by
comparing with several state-of-the-art methods. The experimental results
demonstrate the effectiveness and efficiency of the proposed method in terms of
the clustering metrics and the computational cost.
