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One of the most fundamental steps in data analysis and dimensionality reduction consists of approximating a given dataset by a single low-dimensional subspace, which is clas-sically achieved via Principal Component Analysis (PCA). However, in many applications, the data often lie near a union of low-dimensional subspaces, reflecting the ...
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