Unsupervised Domain Adaptation Method Based on Discriminant Sample Selection

基于判别性样本选择的无监督领域自适应方法

School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710072, China

Received: 23 October 2019

Abstract

In order to solve the problem that low classification accuracy caused by the different distribution of training set and test set, an unsupervised domain adaptation method based on discriminant sample selection (DSS) is proposed. DSS projects the samples of different domains onto a same subspace to reduce the distribution discrepancy between the source domain and the target domain, and weights the source domain instances to make the samples more discriminant. Different from the previous method based on the probability density estimation of samples, DSS tries to obtain the sample weights by solving a quadratic programming problem, which avoids the distribution estimation of samples and can be applied to any fields without suffering from the dimensional trouble caused by high-dimensional density estimation. Finally, DSS congregates the same classes by minimizing the intra-class distance. Experimental results show that the proposed method improves the classification accuracy and robustness.

摘要

针对现实中由于训练集与测试集分布不同而导致分类准确率较低的问题，提出基于判别性样本选择的无监督领域自适应方法（简称DSS算法）。为了减少源域和目标域的分布差异，将2个领域样本投影到同一子空间中，并对源域中的样本进行加权，使样本更具有判别性；不同于以往基于样本的概率密度估计方法，通过求解一个二次规划问题得到样本权重，避免了对样本分布进行估计，适用于任何领域且不会受到高维密度估计所造成的维数困扰；最后通过最小化类内距离来实现同类聚集。实验结果表明，该方法提高了数据集的分类准确率且具有较好的鲁棒性。