智能系统学报2017，Vol.12Issue(3)：325-332,8.DOI:10.11992/tis.201704024
应用k-means算法实现标记分布学习
Label distribution learning based on k-means algorithm
摘要
Abstract
Label distribution learning is a new type of machine learning paradigm that has emerged in recent years.It can solve the problem wherein different relevant labels have different importance.Existing label distribution learning algorithms adopt the parameter model with conditional probability, but they do not adequately exploit the relation between features and labels.In this study, the k-means clustering algorithm, a type of prototype-based clustering, was used to cluster the training set instance since samples having similar features have similar label distribution.Hence, a new algorithm known as label distribution learning based on k-means algorithm (LDLKM) was proposed.It firstly calculated each cluster's mean vector using the k-means algorithm.Then, it got the mean vector of the label distribution corresponding to the training set.Finally, the distance between the mean vectors of the test set and the training set was applied to predict label distribution of the test set as a weight.Experiments were conducted on six public data sets and then compared with three existing label distribution learning algorithms for five types of evaluation measures.The experimental results demonstrate the effectiveness of the proposed KM-LDL algorithm.关键词
标记分布/聚类/k-means/闵可夫斯基距离/多标记/权重矩阵/均值向量/softmax函数Key words
label distribution/clustering/k-means/Minkowski distance/multi-label/weight matrix/mean vector/softmax function分类
信息技术与安全科学引用本文复制引用
邵东恒,杨文元,赵红..应用k-means算法实现标记分布学习[J].智能系统学报,2017,12(3):325-332,8.基金项目
国家自然科学基金项目(61379049, 61379089). （61379049, 61379089）
