{'fraction_non_alphanumeric': 0.02527075812274368, 'fraction_numerical': 0.0, 'mean_word_length': 5.351145038167939, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Divide-and-Conquer Subspace Segmentation

Several important computer vision tasks have recently been formulated as lowrank problems, with the Low-Rank Representation method (LRR) being one recent and prominent formulation. Although the method is framed as a convex program, available solutions to this program are inherently sequential and costly, thus limiting its scalability. In this work, we explore the effectiveness of a recently introduced divide-and-conquer framework, entitled DFC, in the context of LRR. We introduce the DFC-LRR algorithm as a scalable solution to the subspace segmentation problem, presenting results that illustrate the scalability and accuracy of DFC relative to LRR. We further present a detailed theoretical analysis that shows that the recovery guarantees of DFC-LRR are comparable to those of LRR.