基于随机近邻嵌入的判别性特征学习

特征学习是机器学习中的一项重要技术,研究从原始数据中学习后置任务所需的数据表示.目前,多数特征学习算法侧重于学习原始数据中的拓扑结构,忽略了数据中的判别信息.基于此,提出了基于随机近邻嵌入的判别性特征学习模型.该模型将对判别信息的学习与对拓扑结构的学习融合在一起,通过迭代求解的方式,同时完成对这两者的学习,从而得到原始数据具有判别性的特征表示,可以显著提升机器学习算法的性能.多个公开数据集上的实验结果验证了该模型的有效性.

Discriminant Feature Learning Based on Stochastic Neighbor Embedding

Feature learning is an important technique in machine learning, which studies learning the data representation required by the post task from raw data. At present, most feature learning algorithms focus on learning the topological structure of the original data, but ignore the discriminant information in the data. This paper proposes a novel model called discriminant feature learning based on t-distribution stochastic neighbor embedding (DTSNE). In this model, the learning of discriminant information and the learning of topology structure are fused together, so both of them are learned to obtain the discriminant feature representation of the original data through iterative solution, which can significantly improve the performance of the machine learning algorithm. Experimental results on multiple open data sets demonstrate the effectiveness of the proposed model.