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Transformed Subspace Clustering
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DOI：10.1109/TKDE.2020.2969354.png)
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摘要
En 中文
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.
Keyword:
Transforms
Kernel
Integrated circuits
Manifolds
Clustering algorithms
Sparse matrices
Symmetric matrices
Transform learning
subspace clustering
image clustering
document clustering
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10.4
论文数:
6.8K
被引数:
3.2W
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