{'fraction_non_alphanumeric': 0.02119460500963391, 'fraction_numerical': 0.0, 'mean_word_length': 5.8355263157894735, '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}}
Parametric eigenspace method using multiple discriminant analysis

Parametric eigenspace methods are well known appearance-based methods for object recognition, which involves object classification and pose estimation. However, ordinary parametric eigenspace methods consider only the expressive features, and they suffer from a problem arising from the fact that discriminative features are not considered. So, there have been developed some methods to construct such eigenspaces considering the discriminative features. However, the method might suffer from another problem, i.e., the so-called generalized eigenvalue problem: yet, we can manage to solve the problem. In this paper, two methods are referred to as representative methods considering discriminative features. Conducting an experiment of object recognition on two similar objects, performances of the methods are compared to one another, and a piece of important knowledge is also presented that the discriminative features are more effective than the expressive features.