Research On Lie Group Metric Meta-learning Algorithm

As a new learning method in the field of machine learning,Lie group machine learning makes full use of the advantage of differential manifold and group of Lie group,which not only provides the geometric representation of data,but also provides a concrete algebraic solution.Meta-learning is a learning method that uses the existing knowledge to quickly learn new and unknown knowledge.The goal of meta-learning is to use the learned information to quickly adapt to new tasks.The development of meta-learning in deep learning provides new solutions to some problems existing in traditional machine learning methods,such as poor robustness and generalization ability,difficulty in rapid adaptation and extreme dependence on large-scale data.In this paper,the metric meta-learning algorithm is deeply explored.Aiming at the problems of the existing methods,such as the inefficiency of feature extraction and feature comparison,the lie group metric meta-learning algorithm is proposed.The main work of this thesis is as follows:(1)A metric meta-learning algorithm based on group equivariant convolution is proposed.The algorithm learns an appropriate metric space through a 4-layer mapping network composed of group equivariant convolution.In this metric space,the samples with the same label are relatively close to each other,while the samples with different labels are relatively far from each other.The classification is completed according to the distance.Finally,the effectiveness of the proposed algorithm is verified by experiments.(2)A metric learning algorithm of Lie group based on data features is proposed.Firstly,a mapping network composed of 4-layer convolutional neural network is constructed.In order to improve the effect of feature extraction,the parameters of the mapping network are orthogonalized,and the network parameters form an orthogonal matrix group.The output feature sets of the mapping network are constructed into a covariance matrix,and then the covariance matrix is transformed into a symmetric positive definite matrix.Logarithmic Euclidean metric is used to measure the distance between sample features,and classification is completed according to the distance.Finally the effectiveness of the proposed algorithm is verified by experiment.(3)Based on the algorithm proposed above,a medical image recognition system is developed in this article.The system is divided into login module,image uploading module and diagnosis module.Once logged in,the user can upload the images to be diagnosed and a set of images with an established condition.The system constructs a meta-task according to the image set uploaded by the user,and inputs the meta-task into the trained model based on the proposed metric meta-learning algorithm of Lie group,and determines the symptoms for the images uploaded by the user according to the prediction results.