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...

Many data applications have certain invariant constraints due to practical needs. Data curators who employ differential privacy need respect such on the sanitized product as a primary utility requirement. Invariants challenge formulation, implementation, and interpretation of guarantees. We propose subspace privacy, honestly characterize dependence output confidential aspects data. discuss two ...