{'fraction_non_alphanumeric': 0.016861219195849545, 'fraction_numerical': 0.0, 'mean_word_length': 5.655172413793103, '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}}
Multi‐view spectral clustering via partial sum minimisation of singular values

This Letter proposes a robust multi-view spectral clustering approach. It first calculates a normalised graph Laplacian for each single view, and then uses them to recover a shared low-rank Laplacian by the low rank and sparse matrix decomposition. To achieve matrix decomposition, partial sum minimisation of singular values is leveraged to design a novel objective function, which can be optimised by the augmented Lagrangian multiplier algorithm to recover a common normalised graph Laplacian. Accordingly, multi-view clustering results can be obtained by taking spectral clustering on the common Laplacian. Experimental results illustrate its effectiveness over other related approaches.