  Recent multi-view subspace clustering achieves impressive results utilizing
deep networks, where the self-expressive correlation is typically modeled by a
fully connected (FC) layer. However, they still suffer from two limitations. i)
The parameter scale of the FC layer is quadratic to sample numbers, resulting
in high time and memory costs that significantly degrade their feasibility in
large-scale datasets. ii) It is under-explored to extract a unified
representation that simultaneously satisfies minimal sufficiency and
discriminability. To this end, we propose a novel deep framework, termed
Efficient and Effective deep Multi-View Subspace Clustering (E$^2$MVSC).
Instead of a parameterized FC layer, we design a Relation-Metric Net that
decouples network parameter scale from sample numbers for greater computational
efficiency. Most importantly, the proposed method devises a multi-type
auto-encoder to explicitly decouple consistent, complementary, and superfluous
information from every view, which is supervised by a soft clustering
assignment similarity constraint. Following information bottleneck theory and
the maximal coding rate reduction principle, a sufficient yet minimal unified
representation can be obtained, as well as pursuing intra-cluster aggregation
and inter-cluster separability within it. Extensive experiments show that
E$^2$MVSC yields comparable results to existing methods and achieves
state-of-the-art performance in various types of multi-view datasets.
