深度卷积神经网络鉴别正交特征生成及其应用

Deep Convolutional Neural Networks Controlled by Discriminatively Orthogonal Feature Generation and Its Application

针对现有深度卷积神经网络在小样本学习时的泛化性问题,本文提出一种鉴别正交特征生成方法.该方法通过正则化技术对网络非负中间层特征输出的异类正交度和同类相关度进行优化,生成具有稀疏特性的网络中间层鉴别正交特征.为有效调节稀疏度以控制网络容量,采用正则化系数自适应调节方式逼近预设特征稀疏度目标.为提高特征生成计算效率,进一步设计了随机2类别鉴别正交特征生成反向传播规则.随后在数据集MNIST上进行了小样本手写体数字识别对比实验,验证了本文方法的稀疏度调节特性和网络表达容量控制能力.通过反卷积可视化,进一步发现本文方法还具有衍生出的局部鉴别区域聚焦特性.最后,将鉴别正交特征生成卷积网络应用到老年痴呆症3D磁共振影像分析上.实验结果表明,本文方法用于老年痴呆症诊断,不仅诊断效果更好,而且利用其良好的局部聚焦性,还成功定位了老年痴呆症与健康对照组典型差异脑区.

To improve the generalization of deep convolutional neural networks(CNN),we proposed a discriminatively orthogonal feature generation method.By regularizing nonnegative outputs,orthogonal degree and correlation degree were optimized simultaneously,which helps to generate discriminatively orthogonal and sparse features.To adjust sparse degree for controlling network capacity,the technique of auto-adjusting regularization coefficient was proposed.To improve computational efficiency,a stochastically 2-class discriminatively orthogonal feature generation rule was further designed.Subsequently,a comparative experiment was conducted on handwritten digit set MNIST.In this experiment,the sparsity adjustment property of our method was verified.By means of deconvolution technique for visualization,it was further found that our method has a good property of focusing on local discriminant areas.Finally,our method was applied to Alzheimer’s Disease MRI image analysis.The experimental results showed that our method outperforms some other representative methods and locates the importantly discriminant brain regions successfully.