Stack autoencoder (SAE), as a representative deep network, has unique and
excellent performance in feature learning, and has received extensive attention
from researchers. However, existing deep SAEs focus on original samples without
considering the hierarchical structural information between samples. To address
this limitation, this paper proposes a new SAE model-neighbouring envelope
embedded stack autoencoder ensemble (NE_ESAE). Firstly, the neighbouring sample
envelope learning mechanism (NSELM) is proposed for preprocessing of input of
SAE. NSELM constructs sample pairs by combining neighbouring samples. Besides,
the NSELM constructs a multilayer sample spaces by multilayer iterative mean
clustering, which considers the similar samples and generates layers of
envelope samples with hierarchical structural information. Second, an embedded
stack autoencoder (ESAE) is proposed and trained in each layer of sample space
to consider the original samples during training and in the network structure,
thereby better finding the relationship between original feature samples and
deep feature samples. Third, feature reduction and base classifiers are
conducted on the layers of envelope samples respectively, and output
classification results of every layer of samples. Finally, the classification
results of the layers of envelope sample space are fused through the ensemble
mechanism. In the experimental section, the proposed algorithm is validated
with over ten representative public datasets. The results show that our method
significantly has better performance than existing traditional feature learning
methods and the representative deep autoencoders.

本文提出了一种新的堆叠自编码器模型 - 邻近信封嵌套堆叠自编码器集成模型（NE_ESAE），该模型使用邻近样本信封学习机制进行优化，采用多层迭代平均聚类方法生成具有层次结构信息的信封样本空间，然后在每个样本空间的层上进行嵌套自编码器（ESAE）的训练和特征减少与基分类器的分类，最终通过集成机制融合信封样本空间的每一层的分类结果，实验结果表明这种方法比传统的特征学习方法和代表性的深度自编码器有显着的性能优势。