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A pseudoinverse learning algorithm for feedforward neural networks with stacked generalization applications to software reliability growth data

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NEUROCOMPUTING 2004年第1期56卷 101-121页

作者： Guo, P Lyu, MR Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China Beijing Normal Univ Dept Comp Sci Beijing 100875 Peoples R China

A supervised learning algorithm, pseudoinverse learning algorithm (PIL), for feedforward neural networks is developed. The algorithm is based on generalized linear algebraic methods, and it adopts matrix inner products and pseudoinverse operations. Incorporating with network architecture of which the number of hidden layer neuron is equal to the number of examples to be learned, the algorithm eliminates learning errors by adding hidden layers and will give an exact solution (perfect learning). Unlike the existing gradient descent algorithm, the PIL is a feedforward only, fully automated algorithm, including no critical user-dependent parameters such as learning rate or momentum constant. The algorithm is tested on case studies with stacked generalization applications to software reliability growth data. The results indicate that the proposed algorithm is very efficient for the investigation on the computation-intensive generalization techniques. (C) 2003 Elsevier B.V. All rights reserved.

关键词： feedforward neural networks generalized linear algebra pseudoinverse learning algorithm fast learning generalization

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A Hierarchical Model with pseudoinverse learning algorithm Optimazation for Pulsar Candidate Selection

A Hierarchical Model with Pseudoinverse Learning Algorithm O...

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IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Li, Shijia Feng, Sibo Guo, Ping Yin, Qian Beijing Normal Univ Image Proc & Pattern Recognit Lab Beijing 100875 Peoples R China

ISBN: (纸本)9781509060177

Pulsars search has always been one of the most concerned problem in the field of astronomy. Nowadays, with the development of astronomical instruments and observation technology, the amount of data is getting bigger and bigger. Radio pulsar surveys have generated and will generate vast amounts of data. To handle big data, developing new technologies and frameworks to efficiently and accurately analyze these data become increasing urgent. The number of positive and negative samples in pulsar candidate data set is very unbalanced, if we only use these a few positive samples to train a deep neural network (DNN), the trained DNN is prone because of the problem of overfitting and will affect the generalization ability. Motivated by the mixtures of experts network architecture, we proposed a hierarchical model for pulsar candidate selection which assembles a set of trained base classifiers. Moreover, training a neural network always takes a lot of time because of using gradient descent (GD) based algorithm. In this work, we utilize the pseudoinverse learning algorithm instead of GD based algorithm to train proposed model. With the designed network architecture and adopted training algorithm, our model has the advantages not only with high steady-state precision but also good generalization performance.

关键词： Pulsar candidate selection pseudoinverse learning algorithm Hierarchical mixtures of experts Big data Stacked generalization

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Broad and pseudoinverse learning for Autoencoder

Broad and Pseudoinverse Learning for Autoencoder

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Xu, Bingxin Guo, Ping Beijing Union Univ Beijing Key Lab Informat Serv Engn Beijing 100101 Peoples R China Beijing Normal Univ Sch Syst Sci Image Pro & Patt Recog Lab Beijing 100875 Peoples R China

ISBN: (纸本)9781538666500

Autoencoder is one approach to automatically learn features from unlabeled data and received significant attention during the development of deep neural networks. However, the learning algorithm of autoencoder suffers from slow learning speed because of gradient descent based algorithms have many drawbacks. pseudoinverse learning algorithm is a fast and fully automated method to train autoencoders. While when the dimension of data is far less than the number of data, the pseudoinverse learning can only obtain the optimal initial value of the autoencoder network and need further learning to achieve satisfactory results. In order to overcome the shortcomings mentioned above, we present a broad learning strategy to transform the input space to the high dimensional space through receptive function in this paper. The transformed data can be more suitable to pseudoinverse learning algorithm which can be obtained the accurate results of autoencoder efficiently. The experimental results show that the proposed method can achieve a comprehensively better performance in terms of training autoencoder efficiency and accuracy.

关键词： Autoencoder pseudoinverse learning algorithm Receptive function Broad learning

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pseudoinverse learning Algorithom for Fast Sparse Autoencoder Training

Pseudoinverse Learning Algorithom for Fast Sparse Autoencode...

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IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Xu, Bingxin Guo, Ping Beijing Union Univ Beijing Key Lab Informat Serv Engn Beijing 100101 Peoples R China Beijing Normal Univ Sch Syst Sci Image Proc & Pattern Recognit Lab Beijing 100875 Peoples R China

ISBN: (纸本)9781509060177

Sparse autoencoder is one approach to automatically learn features from unlabeled data and received significant attention during the development of deep neural networks. However, the learning algorithm of sparse autoencoder suffers from slow learning speed because of gradient descent based algorithms have many drawbacks. In this paper, a fast learning algorithm for sparse autoenceder is proposed which based on pseudoinverse learning algorithm (PIL). The proposed method calculates encoder weight matrix by truncating the pseudoinverse matrix of input data. The pseudoinverse truncation matrix is used as the weights of encoder, and then the input data is mapped to the hidden layer space through the biased ReLU activation function. The decoder weights are also can computed by the PIL. Unlike the gradient descent based algorithm, the proposed method does not require a time-consuming iterative optimization process and select many user-dependent parameters such as learning rate or momentum constant too. The experimental results indicate the superiority of proposed method which is very efficient and also can learned the sparsity of samples.

关键词： sparse autoencoder pseudoinverse learning algorithm fast learning data representation

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An efficient and effective deep convolutional kernel pseudoinverse learner with multi-filter

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NEUROCOMPUTING 2021年 457卷 74-83页

作者： Deng, Xiaodan Mahmoud, Mohammed A. B. Yin, Qian Guo, Ping Beijing Normal Univ Sch Syst Sci Image Proc & Pattern Recognit Lab Beijing 100875 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Beijing Normal Univ Sch Artificial Intelligence Image Proc & Pattern Recognit Lab Beijing 100875 Peoples R China

The convolutional neural network is the most widely used deep neural network. However, it still has some disadvantages. First, the back-propagation method is usually used in the training of convolutional neural networks, but it has several inherent defects, such as the vanishing gradient problem and exploding gradient problem. Moreover, the training of a convolutional neural network often needs substantial computational resources and time. To solve the above problems, based on pseudoinverse learning (PIL), kernel pseudoinverse learning (KPIL) is proposed, showing improved performance. The number of hidden layer neurons of KPIL is equal to the number of input data and does not need to be set. KPIL uses the kernel method to calculate the output of hidden layer, avoiding the uncertainty of random input weights. Based on KPIL, a deep convolutional kernel pseudoinverse learner with a multi-filter design is proposed, named kernel pseudoinverse learning convolutional neural network (KPIL-CNN), which uses multiple fixed convolutional kernels: kernel pseudoinverse learning filters (KPIL filter), Gabor filters, and random filters. The features obtained by this multi-filter approach are combined into feature maps by learning weights. The experimental results show that the proposed KPIL-CNN performs better than other network models with the same scale in the task of natural image classification. The training of KPIL-CNN is efficient and effective. Furthermore, KPIL-CNN outperforms the existing methods in the classification of the ISIC 2017 skin lesion dataset. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Kernel pseudoinverse learner Multi-filter pseudoinverse learning algorithm Fast training of CNN

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pseudoinverse learning Algorithom for Fast Sparse Autoencoder Training

Pseudoinverse Learning Algorithom for Fast Sparse Autoencode...

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IEEE Congress on Evolutionary Computation

作者： Bingxin Xu Ping Guo Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

关键词： sparse autoencoder pseudoinverse learning algorithm fast learning data representation

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