  Subspace clustering has been extensively studied from the
hypothesis-and-test, algebraic, and spectral clustering based perspectives.
Most assume that only a single type/class of subspace is present.
Generalizations to multiple types are non-trivial, plagued by challenges such
as choice of types and numbers of models, sampling imbalance and parameter
tuning. In this work, we formulate the multi-type subspace clustering problem
as one of learning non-linear subspace filters via deep multi-layer perceptrons
(mlps). The response to the learnt subspace filters serve as the feature
embedding that is clustering-friendly, i.e., points of the same clusters will
be embedded closer together through the network. For inference, we apply
K-means to the network output to cluster the data. Experiments are carried out
on both synthetic and real world multi-type fitting problems, producing
state-of-the-art results.
