Subspace clustering assumes that the data is separable into separate subspaces. Such a simple assumption, does not always hold. We assume that, even if raw subspaces, one can learn representation (transform coefficients) such learnt To achieve intended goal, we embed subspace techniques (locally linear manifold clustering, sparse and low rank representation) transform learning. The entire formu...