矮新星是一类特殊而稀少的半相接双星。 发现更多的矮新星对于深入研究物质转移理论、 理解密近双星演化过程意义深远。 利用深度学习技术提取天体光谱特征并进而分类是天文数据处理领域的研究热点。 传统的自编码器是仅包含一个隐层的经典神经网络模型, 编码能力有限, 数据表征学习能力不足。 模块化拓宽神经网络的深度能够驱使网络继承地学习到天体光谱的特征, 通过对底层特征的逐渐抽象学习获得高层特征, 进而提高光谱的分类准确率。 以自编码器为基础构建了由输入层、 若干隐藏层和输出层组成的基于多层感知器架构的深度前馈堆栈式自编码器网络, 用于处理海量的光谱数据集, 挖掘隐藏在光谱内部具有区分度的深度结构特征, 实现对矮新星光谱的准确分类。 鉴于深度架构网络的参数设置会严重影响所构建网络的性能, 将网络参数的优化分为逐层训练和反向传播两个过程。 预处理后的光谱数据先由输入层进入网络, 再经自编码器算法和权值共享实现对网络参数的逐层训练。 反向传播阶段将初始样本数据再次输入网络, 以逐层训练所得的权值对网络初始化, 再把网络各层的局部优化训练结果融合起来, 根据所设置的输出误差代价函数调整网络参数。 反复地逐层训练和反向传播, 直到获得全局最优的网络参数。 最后由末隐层作为重构层搭建支持向量机分类器, 实现对矮新星的特征提取与分类。 网络参数优化过程中利用均值网络思想使网络隐层单元输出按照dropout系数衰减, 并由反向传播算法微调整个网络, 从而防止发生深度过拟合现象, 减少因隐层神经元间的相互节制而学习到重复的数据表征, 提高网络的泛化能力。 该网络分布式的多层次架构能够提供有效的数据抽象和表征学习能力, 其特征检测层可从无标注数据中隐式地学习到深度结构特征, 有效刻画光谱数据的非线性和随机波动性, 避免了光谱特征的显式提取, 体现出较强的数据拟合和泛化能力。 不同层之间的权值共享能够减少冗余信息的干扰, 有效化解传统多层次架构网络易陷入权值局部最小化的风险。 实验表明, 该深度架构网络在矮新星分类任务中能达到95.81%的准确率, 超过了经典的LM-BP网络。

Dwarf nova (DN) is a special and rare class of semi-contiguous binary star. To discovery more DNs is significant for the further study of matter transfer theory. It also has been profound for understanding the evolution of close binary stars. It is a research hot spot to extract features of celestial spectra and then classify them by deep learning. Traditional auto-encoder is a classical neural network model with only one hidden layer. However, its coding ability is limited and data representation learning ability is insufficient. Broadening the depth of the neural network with modularity can make the network learn features of the celestial spectrum successively. High-level features can be obtained through gradual abstract learning of underlying features so as to improve the spectral classification accuracy. In this paper, a deep feedforward stack network is constructed consisting of an input layer, several hidden layers and an output layer on the basis of auto-encoder. This network with multi-layer perceptron architecture is utilized to process massive spectral data sets. It excavates the depth structure features hidden in the spectra and realizes the accurate classification of DN spectra. Parameters set for the network with deep architecture will seriously affect the performance of the constructed network. In this paper, the optimization of network parameters is divided into two processes: hierarchical training and inverse propagation. The preprocessed spectral data first enter the network from the input layer, and then the network parameters are trained layer by layer with the auto-encoder algorithm and weight sharing policy. In the reverse propagation stage, the initial sample data are input into the network again, and the network is initialized with the weights obtained from the hierarchical training process. Then the local optimization training results of each layer are fused and the network parameters are adjusted according to the set output error cost function. Hierarchical training and inverse propagation are operated repeatedly until the global optimal network parameters are obtained. Finally, the last hidden layer is adopted as the reconstruction layer to connect the support vector machine classifier, and the feature extraction and classification of DNs are realized. In the process of network parameter optimization, the idea of mean network is utilized to make the output of network hidden layer unit attenuate according to dropout coefficient. The reverse propagation algorithm is adopted to fine-tune the entire network to prevent depth overfitting in the network. Such operation can reduce to extract duplicate feature caused by mutual moderation of hidden layer neurons, and improve the generalization ability. The distributed multi-layer architecture of the network can provide effective data abstraction and representational learning. The feature detection layer can learn the depth structure features implicitly from the unlabeled data, and effectively characterize the nonlinearity and random fluctuation of spectral data, thus avoiding the explicit extraction of spectral features. The network shows strong data fitting and generalization ability. Weight sharing between different layers can reduce the interference of redundant information and effectively resolve the risk that the traditional multi-layer architecture network is prone to fall into the local minimization of weight. Experiments show that that of the accuracy of the deep architecture network in DNs classification is 95.81%, higher than the classical LM-BP network.