融合图嵌入的光滑主成分分析网络图像识别算法

主成分分析网络(principal component analysis network, PCANet)是一种简单的深度学习算法,在图像识别领域具有优秀的性能。将图嵌入思想融入PCANet,提出一种新的图像识别算法光滑主成分分析网络(Smooth-PCANet)。为了验证Smooth-PCANet算法的有效性,在人脸、手写体字符以及图片等不同数据集上构建实验,并将Smooth-PCANet与多种基于深度学习的图像识别算法作了对比。实验结果证明,Smooth-PCANet算法比PCANet获得了更高的识别性能,并且更有效地避免了过拟合,在小样本训练时具有显著优势。

Smooth principal component analysis network image recognition algorithm with fusion graph embedding

PCANet (principal component analysis network) is a simple deep learning algorithm with excellent performance in the field of image recognition. Integrating the idea of graph embedding into PCANet, a new image recognition algorithm Smooth-PCANet was proposed. In order to verify the effectiveness of the Smooth-PCANet algorithm, adequate experiments were performed on different data sets such as face, handwritten characters, and images. Compared with several image recognition algorithms based on deep learning, the experiments demonstrated that the Smooth-PCANet achieves higher recognition performance than the PCANet and avoids overfitting more effectively, with a significant advantage in small samples training.