基于量表大数据的深度神经网络抑郁分类模型

针对抑郁相关量表大数据所面临的特征冗余、特征维度单一、特征子集难以确定等问题，提出了一种基于深度神经网络（deep nural network,DNN）的抑郁分类模型．通过结合主成分投影k-means（principal component, PC k-means），在不破坏原有特征空间的条件下对量表大数据进行特征选择，并对原始算法的随机性与聚类个数不确定的问题进行了优化；在此基础上，为了增强抑郁识别维度的多样性，构建了引入因子分解机（factorization machines，FM）的DNN抑郁分类模型．分析和对比结果表明:PC k-means不仅可以有效地选择特征，而且与传统分类器和FM-DNN结合，更能提高抑郁分类的准确率，为深度学习引入量表大数据分析提供了新的研究思路与方向． 

Deep neural network depression classification model based on by scale big data

We propose a depression classification model of deep neural network to solve problems of feature redundancy, single feature dimensions and difficulty to determine feature subset in big data of depression related scale．By combining principal component k-means algorithm (PC k-means), we selected features of scale big data without destroying original feature space, and optimized randomness of original algorithm and uncertainty of number of clusters．To enhance diversity of depression recognition dimensions, a deep neural network depression classification model with factor decomposition machine (FM-DNN) was constructed．Analysis and comparison showed that PC k-means could not only effectively select features, but also improve accuracy of depression classification by combining with traditional classifier and FM-DNN．This work provides new ideas and direction for introduction of scale big data analysis into deep learning．