Transformed Subspace Clustering
This work addresses clustering challenges in domains like image and document analysis, but it is incremental as it builds on existing subspace clustering and transform learning methods.
The authors tackled the problem of subspace clustering when raw data is not separable into subspaces by learning a transformed representation that becomes separable, resulting in improved performance compared to state-of-the-art clustering techniques on image and document datasets.
Subspace clustering assumes that the data is sepa-rable into separate subspaces. Such a simple as-sumption, does not always hold. We assume that, even if the raw data is not separable into subspac-es, one can learn a representation (transform coef-ficients) such that the learnt representation is sep-arable into subspaces. To achieve the intended goal, we embed subspace clustering techniques (locally linear manifold clustering, sparse sub-space clustering and low rank representation) into transform learning. The entire formulation is jointly learnt; giving rise to a new class of meth-ods called transformed subspace clustering (TSC). In order to account for non-linearity, ker-nelized extensions of TSC are also proposed. To test the performance of the proposed 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.