Transformed Subspace Clustering

Subspace clustering assumes that the data is separable into separate subspaces. Such a simple assumption, does not always hold. We assume that, even if the raw data is not separable into subspaces, one can learn a representation ( transform coef-fi cients) such that the learnt representation is separable into subspaces. To achieve the intended goal, we embed subspace clustering techniques (locally linear manifold clustering, sparse subspace clustering and low rank representation) into transform learn ing. The entire formulation is jointly learnt; giving rise to a new class of methods called transformed subspace clustering (TSC). In order to account for non - linearity, ker-nelized extensions of TSC are also proposed. To test the performanc e of the propose d techniques, benchmarking is performed on image clustering and document clustering datasets. Comparison with state - of - the - art clustering techniques shows that our formulation improves upon them.