基于深度学习的边际Fisher分析特征提取算法

Marginal Fisher Feature Extraction Algorithm Based on Deep Learning

提取符合数据分布结构的特征一直是模式识别领域的热点问题。基于固定核映射方法具有获取非线性特征的能力,但对映射函数类型及其参数十分敏感。论文提出一种基于多层自动编码器的特征提取算法,该深度学习网络模型的训练分为无监督预训练以及基于边际Fisher准则的监督式精雕训练过程。通过数据生成性预训练和精雕过程中正则化手段防止过拟合训练。在多个数据集进行分类的实验结果进一步验证算法的有效性。

It is always important issue to extract features that are most effective for preserving the distribution architecture in pattern recognition community. Kernel based methods are assumed to extract nonlinear features. However, it is very sensitive to the selection of its mapping function and parameters. This paper proposes a feature extraction algorithm based on multi-layer auto-encoder, which consists of two phases of unsupervised pretraining and supervised fine-tuning based on marginal Fisher rule. Generative pretraining and regularization methods within fine-tuning phase are adopted to avoid overfitting of model＇s training. The validity of algorithm is proved within the result of classification experiments in several datasets.