{'fraction_non_alphanumeric': 0.0225140712945591, 'fraction_numerical': 0.0, 'mean_word_length': 5.592592592592593, '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}}
Spectral Methods for the Hierarchical Dirichlet Process

The Hierarchical Dirichlet Process (HDP) is a versatile, albeit computationally expensive tool for statistical modeling of mixture models. In this paper, we introduce a spectral algorithm. We show that it is both computationally and statistically efficient. In particular, we derive the lower-order moments of the HDP and give reconstruction guarantees. Moreover, we show that hierarchical spectral method is able to generate a better results regarding likelihood performance.