基于改进图正则项的自编码器特征学习算法

传统的图正则化方法使用欧氏距离度量样本空间的相似度，并不能准确考察复杂数据集的邻域信息，容易导致模型在复杂形状数据和非凸数据集中的泛化性能下降。提出一种改进的图正则算法，使用等距特征映射保留样本空间的邻域信息，帮助模型进行流形学习，同时结合使用KL约束进一步使得数据表示的外部结构变得光滑，从而捕获到更稀疏和高级的特征表示。在MNIST和YaleB等数据集上的实验结果表明，相比于流行的几种特征提取算法，该算法能够提取到更有意义和稳健的特征。在分类任务和聚类任务上具有优势，同时具有更好的抗干扰性能。

Feature learning method based on improved graph regularized auto-encoder

Traditional graph regularization methods use Euclidean distance to measure the similarity of sample space, and can not accurately preserve the neighborhood information of complex data sets, which easily lead to the degradation of the generalization performance of the model in complex shape data and non convex data sets. This paper proposed an improved graph regularization algorithm, which used isometric feature mapping to preserve the neighborhood information of the sample space and help the model learn manifold. The simultaneous used of KL constraints further smoothed the external structure of the data representation, thereby capturing more sparse and advanced feature representations. Experimental results on MNIST and YaleB datasets show that compared with several popular feature extraction algorithms, the proposed algorithm can extract more mea-ningful and robust features. It has advantages in classification and clustering tasks, and also has better anti-interference capability.