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Robust, Scalable, and Provable Approaches to High Dimensional Unsupervised Learning

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Figure 2.2: Data distributions in a two-dimensional subspace. The blue stars and red circles are the data points and their projections on the unit circle, respectively
Figure 2.6: Left: Phase transition for various coherency parameters and dimension of the in- in-tersection
Figure 2.7: Left: Phase transition for different values of n 1 and n 2 , the number of data points in the first and second subspaces
Figure 2.8: Performance of the algorithms versus the dimension of intersection for different noise levels.
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