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Transformed Subspace Clustering
[article]
2019
arXiv
pre-print
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
arXiv:1912.04734v1
fatcat:pfauacjv3zff5mitqo2mc7ew3e