  We propose Deep Closed-Form Subspace Clustering (DCFSC), a new embarrassingly
simple model for subspace clustering with learning non-linear mapping. Compared
with the previous deep subspace clustering (DSC) techniques, our DCFSC does not
have any parameters at all for the self-expressive layer. Instead, DCFSC
utilizes the implicit data-driven self-expressive layer derived from
closed-form shallow auto-encoder. Moreover, DCFSC also has no complicated
optimization scheme, unlike the other subspace clustering methods. With its
extreme simplicity, DCFSC has significant memory-related benefits over the
existing DSC method, especially on the large dataset. Several experiments
showed that our DCFSC model had enough potential to be a new reference model
for subspace clustering on large-scale high-dimensional dataset.
