含类信息的极限学习机自动编码器特征学习方法

Feature learning method of extreme learning machine auto-encoder with category information

极限学习机自动编码器(ELM-AE)将极限学习机(ELM)技术与自动编码器(AE)结合,可以无监督学习数据特征且克服了参数迭代调整的昂贵时间消耗。然而,以最小化重构误差为目标的ELM-AE并不能有效利用分类问题中的数据类别信息,导致特征的类别可分性较差。针对此现象,本文提出一种面向数据分类的含类信息极限学习机自编码(CELM-AE)特征学习方法,该方法将投影特征向量的类间离散度与类内相似度限制到ELM-AE的目标函数中,且可通过解析算法求得更具类别分辨力的最优数据表示。对6种UCI数据集分别使用基于CELM-AE、ELM-AE和AE的特征表示进行分类实验,结果表明,CELM-AE得到的数据特征在两种分类器(ELM/KNN)下的分类精度与稳定性表现均优于ELM-AE与AE,且时间代价很小,说明了CELM-AE在提取可分性数据特征表示方面的优势。

Extreme learning machine auto-encoder(ELM-AE) combines extreme learning machine(ELM) technology with auto-encoder(AE), which can learn data features unsupervised and overcome the expensive time consumption of parameter iterative adjustment. However, ELM-AE, which aims to minimize reconstruction errors, cannot effectively use the data category information in classification problems, resulting in features with poor category separability. In view of this phenomenon, this paper proposes a data classification-oriented feature learning method of extreme learning machine auto-encoder with category information(CELM-AE), which limits the inter class dispersion and intra class similarity of the projected feature vector to the objective function of ELM-AE, and can obtain the optimal data representation with more class resolution through analytical algorithm. The classification experiments of 6 UCI data sets are carried out using the feature representation based on CELM-AE, ELM-AE and AE respectively. The results show that the classification accuracy and stability of the data features obtained by CELM-AE under the two classifiers(ELM/KNN) are better than ELM-AE and AE, and the time cost is very small, which shows the advantages of CELM-AE in extracting the separable feature representation of data.