混杂数据的多核几何平均度量学习

发布时间：2018-05-11 21:18

本文选题：几何平均 + 多核学习　；参考：《软件学报》2017年11期

【摘要】：在机器学习和模式识别任务中,选择一种合适的距离度量方法是至关重要的.度量学习主要利用判别性信息学习一个马氏距离或相似性度量.然而,大多数现有的度量学习方法都是针对数值型数据的,对于一些有结构的数据(比如符号型数据),用传统的距离度量来度量两个对象之间的相似性是不合理的;其次,大多数度量学习方法会受到维度的困扰,高维度使得训练时间长,模型的可扩展性差.提出了一种基于几何平均的混杂数据度量学习方法.采用不同的核函数将数值型数据和符号型数据分别映射到可再生核希尔伯特空间,从而避免了特征的高维度带来的负面影响.同时,提出了一个基于几何平均的多核度量学习模型,将混杂数据的度量学习问题转化为求黎曼流形上两个点的中心点问题.在UCI数据集上的实验结果表明,针对混杂数据的多核度量学习方法与现有的度量学习方法相比,在准确性方面展现出更优异的性能.
[Abstract]:In the task of machine learning and pattern recognition, it is very important to choose a suitable distance measurement method. Metric learning mainly uses discriminant information to learn a Markov distance or similarity measure. However, most of the existing metric learning methods are for numerical data. For some structured data, such as symbolic data, it is unreasonable to measure the similarity between two objects by traditional distance measurement. Most metric learning methods are troubled by the dimension, the high dimension makes the training time long, and the scalability of the model is poor. A hybrid data metric learning method based on geometric averaging is proposed. Different kernel functions are used to map numerical data and symbolic data to Hilbert space of reproducing kernel respectively, thus avoiding the negative effects of the high dimension of features. At the same time, a multi-kernel metric learning model based on geometric averaging is proposed, which transforms the metric learning problem of hybrid data into the center point problem of two points on Riemannian manifold. The experimental results on the UCI dataset show that the multi-core metric learning method for hybrid data shows better performance in accuracy than the existing metric learning methods.
【作者单位】：天津大学计算机科学与技术学院;
【分类号】：TP181

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