线性判别分析
计算机科学
模式识别(心理学)
人工智能
非负矩阵分解
矩阵分解
稳健性(进化)
特征提取
分类器(UML)
散射矩阵
算法
协方差矩阵
特征向量
化学
协方差矩阵的估计
生物化学
物理
量子力学
基因
作者
Jitao Zhong,Zhengyang Shan,Xuan Zhang,Haifeng Lu,Hong Peng,Bin Hu
标识
DOI:10.1016/j.bspc.2022.104505
摘要
The incidence of depression has recently increased significantly. However, the current manual diagnosis may delay real-time detection and early treatment. Therefore, an automatic and effective auxiliary diagnosis is urgent. For automatic depression recognition, this paper presents a novel feature extraction algorithm, namely, Robust Discriminant Non-negative Matrix Factorization (RDNMF), which is joint optimization of the measurement of ℓ2,1-norm, within-class scatter distance and between-class scatter distance. Different from traditional Non-negative Matrix Factorization (NMF) that just decomposes one high dimension matrix into the product of two new low dimension matrices, i.e. basic matrix and coefficient matrix, our algorithm also considers the robustness and discriminant of these two matrices, which can enhance the representation capability of basic matrix and significantly improve classification performance compared to other comparative methods. In addition, we have designed an audio stimuli paradigm for the measurement of functional Near-Infrared Spectroscopy (fNIRS) in task-state experiment. Finally, under the negative audio stimuli, our algorithm has promising results with random forest classifier, that is, Accuracy of 96.4%, Specificity of 100%, Sensitivity of 95.0% and AUC of 93.5%, which are superior in comparison with comparative machine learning methods, and simultaneously have comparable potential to state-of-the-art neural networks. Moreover, results also show that recognition rate of depression is highest under negative audio stimuli, which makes it possible to extract prominent features with this algorithm for auxiliary diagnosis of depression.
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