{'fraction_non_alphanumeric': 0.020553359683794466, 'fraction_numerical': 0.0, 'mean_word_length': 5.525773195876289, '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}}
Kernel Principal Component Analysis Pseudoinverse Learning Algorithm

Feedforward neural network has been widely used in many fields, but using back-propagation method to train a neural network still has some disadvantages, such as vanishing or exploding gradient problem. Among the algorithms for solving these problems, the pseudoinverse learning (PIL) algorithm was noted for its significant advantages, such as low training error, no local minimum, no parameter selection, etc. However, the PIL algorithm still suffers from over-fitting problem and lack of learning ability because of its network structure. Focusing on these drawbacks, this paper proposes a novel algorithm named the Kernel Principal Component Analysis PIL (KPCAPIL) algorithm which integrates kernel principal component analysis (KPCA) into the PIL algorithm. It firstly calculates the features of the hidden layer with the KPCA then uses the kernel method and transformation matrix as weight parameters from the input layer to the hidden layer. Moreover, the weight parameters from the hidden layer to the output layer are calculated by the PIL algorithm. The KPCAPIL algorithm achieves better performance than the PIL algorithm, the PIL algorithm with regularization and the KPIL algorithm.