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• W3002481680 abstract "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 coefficients) 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 learning. 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, kernelized 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." @default.
• W3002481680 created "2020-01-30" @default.
• W3002481680 creator A5020310463 @default.
• W3002481680 creator A5061490660 @default.
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• W3002481680 date "2021-04-01" @default.
• W3002481680 modified "2023-10-14" @default.
• W3002481680 title "Transformed Subspace Clustering" @default.
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• W3002481680 doi "https://doi.org/10.1109/tkde.2020.2969354" @default.
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