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We consider the problem of simultaneously clustering and learning a linear representation of data lying close to a union of low-dimensional manifolds, a fundamental task in machine learning and computer vision.
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Generalized principal component analysis
René Vidal, Yi Ma, and S Shankar Sastry · 2016
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Noisy sparse subspace clustering
Yu-Xiang Wang and Huan Xu · 2016
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Neural collapse with unconstrained features
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Prevalence of neural collapse during the terminal phase of deep learning training
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