{'fraction_non_alphanumeric': 0.0148975791433892, 'fraction_numerical': 0.0, 'mean_word_length': 5.255813953488372, '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}}
A Comparative Study of Dimensionality Reduction Methods: The Case of Music Similarity

In this paper, we investigate the performance of three unsupervised classification algorithms applied to musical data. They are first evaluated on the direct set of feature vectors that have been extracted from the original songs, and we try to highlight whether this data seems to lie on an embedded manifold or not. Furthermore, we try to enhance the obtained results by applying preprocessing transformations to the data, with encouraging results.