{'fraction_non_alphanumeric': 0.015503875968992248, 'fraction_numerical': 0.0, 'mean_word_length': 5.796992481203008, '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}}
Density Function Interpretation of Subspace Classification Methods

Subspace methods are a powerful class of statistical pattern classiication algorithms. They are traditionally considered nonparametric or semiparametric models, which means that the classiier does not produce the a posteriori probability of the input vector. This paper, however, presents a novel density estimation interpretation for the subspace methods. As a byproduct, the problem of unequal priors, which occurs if standard subspace methods are used when the a priori probabilities of the classes are unequal, is eliminated. As well, the a posteriori probability produced by the suggested method can be used to implement a plausible rejection method for diicultly classiiable patterns. At the end of this presentation, the applicability of the proposed probability density function interpretation is demonstrated with experiments.