降噪分层映射算法在多维聚类分析中的优化研究

为了在多维聚类分析中运用有效的深度特征选择方法排除冗余和无关的特征属性,学习数据元素的非线性关系提取最佳特征,提出一种降噪分层映射算法(DHM).首先,基于降噪自动编码器构建非循环神经网络,容错数据经过隐藏层加权和激活函数的训练获取输入数据的非线性关系得到特征空间,实现特征重构选取最佳特征.其次,特征空间用于调整自组织特征映射神经网,通过计算最小化加权平方欧式距离寻找匹配的获胜神经元.最后,结合特征选择网络和无监督聚类网络为降噪分层映射神经网,通过整体模型迭代训练,使权重参数和偏差向量同时得到优化,实现有效的无监督聚类方案.在真实数据集上的实验结果表明,同AESOM,DCSOM和S-SOM算法相比,DHM算法在提高聚类质量及准确性方面有更好的表现.

Optimization research of denoised hierarchical mapping analysis for multidimensional cluster analysis

A de-noising hierarchical mapping (DHM) algorithm is proposed in order to use effective deep feature selection methods in multi-dimensional clustering analysis to eliminate redundant and irrelevant features and learn the nonlinear relationship of data elements to extract the best features. In the algorithm, an acyclic neural network is first built based on the denoising autoencoder. Specifically, the fault tolerant data are trained by hidden layer weighting and activation function to obtain the nonlinear relationship of the input data and the feature space. The features are reconstructed and the best features are selected. Secondly, the feature space is used to adjust the self-organizing feature map neural network. the minimized weighted squared Euclidean distance is calculated to find the matching winning neuron. Finally, the feature selection network and the unsupervised clustering network are combined to construct the noise reduction hierarchical map neural network. The noise reduction hierarchical map neural network is iteratively trained and the weight parameter and the deviation vector are optimized at the same time to realize an effective unsupervised clustering scheme. Experimental results on real data sets show that, compared with AESOM, DCSOM and S-SOM algorithms, the DHM algorithm has better performance in the quality and accuracy of clustering.