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- W2891918059 abstract "Transform learning is a relatively new analysis formulation for learning a basis to represent signals. This work incorporates the simplest subspace clustering formulation - Locally Linear Manifold Clustering, into the transform learning formulation. The core idea is to perform the clustering task in a transformed domain instead of processing directly the raw samples. The transform analysis step and the clustering are not done piecemeal but are performed jointly through the formulation of a coupled minimization problem. Comparison with state-of-the-art deep learning-based clustering methods and popular subspace clustering techniques shows that our formulation improves upon them." @default.
- W2891918059 created "2018-09-27" @default.
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- W2891918059 date "2018-09-01" @default.
- W2891918059 modified "2023-09-30" @default.
- W2891918059 title "Transformed Locally Linear Manifold Clustering" @default.
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- W2891918059 doi "https://doi.org/10.23919/eusipco.2018.8553061" @default.
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