{'fraction_non_alphanumeric': 0.016252390057361378, 'fraction_numerical': 0.0, 'mean_word_length': 5.711538461538462, '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}}
Robust Online Tucker Dictionary Learning from Multidimensional Data Streams

Big data streaming analytics has recently attracted much attention in the signal and information processing communities due to the fact that massive streaming datasets have been collected over the years. Among them, many modern data streams are represented as multidimensional arrays (aka tensors), and thus, streaming tensor decomposition or tensor tracking has become a promising tool to analyze such streaming data. In this paper, we propose a novel online algorithm called ROTDL for the problem of robust tensor tracking under the Tucker format. ROTDL is not only capable of tracking the underlying Tucker dictionary of multidimensional data streams over time, but also robust to sparse outliers. The proposed algorithm is specifically designed by using the alternating direction method of multipliers, block-coordinate descent, and recursive least-squares filtering techniques. Several experiments demonstrate the effectiveness of ROTDL for robust tensor tracking.