特征聚类自适应变组稀疏自编码网络及图像识别

由于缺乏先验信息，组Lasso模型在训练时仅是基于组数参数对单元进行均匀、连续、固定的分组，缺乏分组依据，容易造成变量组结构的有偏估计。为此，提出特征聚类自适应变组稀疏自编码网络模型，在迭代过程中使用特征聚类法来改变隐层单元的分组，使得分组能够随着特征的收敛而自适应地发生改变，从而更好地实现变量组结构的估计。实验表明，该模型能够很好地捕捉训练过程中出现的组相关信息，并在一定程度上提高图像的分类识别率。

Image recognition based on feature clustering adaptive sparse group autoencoder

Due to the lack of prior information, the group Lasso model is trained based on the group number parameter that groups the units uniformly, continuously and fixedly, which easily leads to biased estimates about the group structure of variables. We propose a feature clustering adaptive sparse group autoencoder, which uses the feature clustering method to change the grouping of the hidden layer unit in the process of iteration so that it can adaptively change with the convergence of the features, achieving better estimation of group structure of the variables. Experiments show that the model can better capture the relevant information of the group structure of the variables during the training process and improve the image classification performance to a certain extent.