使用无标签数据的主成分初始化方法

Principal Component Initialization Method With Unlabeled Data

为了解决初始化方法的不确定性以及有标签数据的不足,提出一种使用无标签数据对网络参数进行主成分初始化的方法,包括采样、主成分计算、初始化和重排卷积核4个步骤.首先,通过移动感受野区域对特征图进行采样,得到与特征图的所有感受野对应的图像块并形成采样集合;然后,计算采样集合的主成分;最后,使用主成分初始化网络参数并重排卷积核,以便提高识别性能.在相同的网络结构和数据集上,即STL-10和CIFAR-10数据集,该方法比传统的初始化方法在识别准确率上提高了4%~20%.实验结果表明,该方法能够充分利用无标签数据初始化网络参数以取得显著的识别效果.此外,通过算法的性能评估,证明该方法明显优于传统的初始化方法.

To solve the problem of uncertainty of initialization methods and insufficient amount of labeled data, an initialization method, which used unlabeled data for principal component initialization of network parameters and included four steps, i. e., sampling, principal component computation, initializing and rearranging the convolutional kernels, was proposed in this paper. First, the feature map was sampled with moving the reception fields, then the image blocks corresponding to all the receptive fields of the feature map were obtained and a sampling set was formed. Second, the principal components of the sampling set were computed. Finally, the principal components were used to initialize the network parameters and the convolutional kernels were rearranged to improve recognition performance. In cases with the same network architecture and datasets, i. e., STL-10 and CIFAR-10 datasets, an increase of recognition accuracy of 4%-20% was obtained by this method in comparison with the traditional initialization methods. The results indicate that the proposed method can make full use of unlabeled data for initializing network parameters to obtain remarkable recognition effectiveness. Moreover, through the performance evaluation of the algorithm, it proves that the method is obviously better than the traditional initialization methods.