近年来,随着人工智能领域技术的不断发展,人机交互领域吸引了更多学者的关注.研究表明由脑电图(electroen-cephalogram,EEG)提取的特征功率谱密度对于脑力负荷的变化比较敏感,但由于其维数过高,容易造成数据灾难.局部线性嵌入(locally linear embedding,LLE)是常用的非线性降维算法,该算法弥补了传统线性降维算法无法发现数据中非线性结构关系的不足.由于不同数据集中样本分布的稀疏程度和扭曲程度不同,在使用LLE对不同数据集进行降维时的最佳邻域参数也不同.利用样本点之间的欧氏距离和测地距离的关系量化了数据集的扭曲程度,自适应邻域参数的局部线性嵌入算法(variable k-locally linear embedding,VK-LLE)动态地调整每一个数据集的最佳邻域参数,解决了样本分布扭曲程度不同对降维效果造成的干扰.实验结果表明,经过VK-LLE降维后的数据使用支持向量机(support vector machine,SVM)分类精度普遍高于经过传统LLE的降维后再使用SVM分类的精度,对复杂数据集有更强的适应能力.
Mental Load Classification of Local Linear Embedding Algorithm Based on Adaptive Neighborhood Parameters
In recent years,with the continuous development of technology in the field of artificial intelligence,the field of human-computer interaction has attracted more scholars'attention.Studies have shown that the characteristic power spectrum density extracted by EEG(electroencephalogram)is sensitive to the change of mental load,but its dimension is too high,which is prone to data disaster.LLE(locally linear embedding)is a commonly used nonlinear dimensionality reduction algorithm,which makes up for the shortcoming of traditional linear dimensionality reduction algorithms that cannot find nonlinear structural relationships in data.Because of the different sparsity and distortion degree of sample distribution in different data sets,the optimal neighborhood parameters are different when using LLE to reduce dimension of different data sets.The relationship between Euclidean distance and geodesy distance between sample points was used to quantify the distortion degree of the data set.VK-LLE(variable k-locally linear embedding)algorithm of adaptive neighborhood parameters dynamically adjusts the optimal neighborhood parameters of each data set.The interference caused by different distortion degree of sample distribution to dimensionality reduction effect was solved.The experimental results show that the classification accuracy of data after VK-LLE dimensionality reduction using SVM(support vector machine)is generally higher than that after traditional LLE dimensionality reduction using SVM,and it has stronger adaptability to complex data sets.
mental loadlocal linear embedding algorithmneighborhood parametergeodesic distance
万方数据
维普
