字典学习通常采用线性函数捕获数据潜在特征, 该方式无法充分提取数据的内在特征结构, 近年来深度学习方法因其强大的特征表示能力而备受关注, 由此本文提出一种结合深度学习与字典学习的非线性特征表示策略, 基于深度神经网络的字典学习(deep neural network-based dictionary learning, DNNDL). DNNDL将字典学习模块融入传统深度学习网络结构中, 在通过自编码器进行映射获取的低维嵌入空间中同时学习数据字典及在其上的稀疏表示系数, 从而实现端到端方式的数据潜在特征提取. DNNDL可为已有数据以及样本外点数据生成紧凑且具判别性的表示. DNNDL不仅是一种新的深度学习网络结构, 并且可将其看作为字典学习和深度学习相结合的统一框架. 通过在4个真实数据集上进行的大量实验, 验证表明所提方法较常用方法具有更好数据表示能力.
Although dictionary learning mostly uses linear functions to capture potential features of data, this method cannot fully extract the inherent feature structure of data. Deep learning has received widespread attention in recent years due to its outstanding feature representation ability. Therefore, this study proposes a nonlinear feature representation strategy combining deep learning with dictionary learning, i.e., deep neural network-based dictionary learning (DNNDL). DNNDL integrates the dictionary learning module into the traditional deep learning network structure and simultaneously learns the data dictionary and the sparse representation coefficients on it in the low-dimensional embedded space mapped by the autoencoder, thereby achieving end-to-end potential data feature extraction. It can generate compact and discriminant representations of existing data as well as out-of-sample point data. DNNDL not only is a new deep learning network structure but also can be regarded as a unified framework of dictionary learning and deep learning. A large number of experiments on four real data sets show that the proposed method has a better data representation capability than those of conventional methods.