关键词: 标记分布学习, 极限学习机, 回归拟合, 核函数

Abstract: Label distribution learning, as a new learning paradigm, uses the specialized algorithm constructed by the maximum entropy model to solve many problems with label ambiguity well, but it is extremely computational intensive. Based on this, the kernel extreme learning machine model with fast running speed and high stability is introduced, and a label distribution learning algorithm based on this model is proposed. Firstly, the features are mapped to high-dimensional space by RBF kernel function in extreme learning machine algorithm. Then, the KELM regression model is established to obtain the output weight for the original label space. Finally, this model is used to predict the label distribution of unknown samples. The comparison between KELM-LDL and the existing algorithm in different sizes of data in various fields show that experimental results of this algorithm are superior to other comparison algorithms, and statistical hypothesis testing further illustrates the effectiveness and stability of the KELM-LDL algorithm.

Key words: label distribution learning, extreme learning machine, regression fitting, kernel function