Humans can use acquired experience to learn new skills quickly and without forgetting the knowledge they already have. However, the neural network cannot do continual learning like humans, because it is easy to fall into the stability-plasticity dilemma and lead to catastrophic forgetting. Since meta-learning with the already acquired knowledge as a priori can directly optimize the final goal, this paper proposes LGCMLA (Lie Group Continual Meta Learning Algorithm) based on meta-learning, this algorithm is an improvement of CMLA (Continual Meta Learning Algorithm) proposed by Jiang et al. On the one hand, LGCMLA enhances the continuity between tasks by changing the inner-loop update rule (from using random initialization parameters for each task to using the updated parameters of the previous task for the subsequent task). On the other hand, it uses orthogonal groups to limit the parameter space and adopts the natural Riemannian gradient descent to accelerate the convergence speed. It not only corrects the shortcomings of poor convergence and stability of CMLA, but also further improves the generalization performance of the model and solves the stability-plasticity dilemma more effectively. Experiments on miniImageNet, tieredImageNet and Fewshot-CIFAR100 (Canadian Institute For Advanced Research) datasets prove the effectiveness of LGCMLA. Especially compared to MAML (Model-Agnostic Meta-Learning) with standard four-layer convolution, the accuracy of 1 shot and 5 shot is improved by 16.4% and 17.99% respectively under the setting of 5-way on miniImageNet.
Feature selection can directly ascertain causes of faults by selecting useful features for fault diagnosis, which can simplify the procedures of fault diagnosis. As an efficient feature selection method, the linear kernel support vector machine recursive feature elimination (SVM-RFE) has been successfully applied to fault diagnosis. However, fault diagnosis is not a linear issue. Thus, this paper introduces the Gaussian kernel SVM-RFE to extract nonlinear features for fault diagnosis. The key issue is the selection of the kernel parameter for the Gaussian kernel SVM-RFE. We introduce three classical and simple kernel parameter selection methods and compare them in experiments. The proposed fault diagnosis framework combines the Gaussian kernel SVM-RFE and the SVM classifier, which can improve the performance of fault diagnosis. Experimental results on the Tennessee Eastman process indicate that the proposed framework for fault diagnosis is an advanced technique. 相似文献
分析了李群流形空间的几何结构、核函数和KFDA(kernel Fisher linear discriminant analysis)的原理,推导了矩阵李群内积空间的度量形式,进一步推导出5个李群核函数,并以此设计实现了KLieDA(kernel Lie group linear discriminant analysis)算法。李群核函数是适应性更广的核函数形式,由于欧氏空间的几何结构是李群的子集,李群函数不仅适用于矩阵李群的样本集,同时也适用于常规的向量形式的样本集。实验表明,基于李群函数和李群均值理论的KLieDA算法是一种快速高效的李群样本分类器。实验部分除了KLieDA的分类,还对基于李群核的SVM(support vector machine)算法进行手写体分类,结果表明,手写体图像的区域协方差李群特征具有较好的线性分布特性。 相似文献