We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (NCAE), that learns features which show part-based representation of data. The learning algorithm is based on constraining negative weights. The performance of the algorithm is assessed based on decomposing data into parts and its prediction performance is tested on three standard image data sets and one text dataset. The results indicate that the nonnegativity constraint forces the autoencoder to learn features that amount to a part-based representation of data, while improving sparsity and reconstruction quality in comparison with the traditional sparse autoencoder and Nonnegative Matrix Factorization. It is also shown that this newly acquired representation improves the prediction performance of a deep neural network.

本文展示了一种新的深度学习自编码器网络，该网络使用非负约束算法（NCAE）进行训练，学习到的特征显示出数据的部分表示。在三个标准图像数据集和一个文本数据集上测试了算法的性能，并发现非负约束强制自编码器学习到数据的部分表示，同时改善了与传统的稀疏自编码器和非负矩阵分解相比的稀疏性和重构质量，进而提高了深度神经网络的预测性能。

使用稀疏自编码器和非负约束进行基于部件表示的数据深度学习