核迁移稀疏编码算法在跨域图像分类中的应用

Kernel Transfer Sparse Coding Algorithms Application for Cross-domain Image Classification

针对非线性分布的数据样本在原始特征空间可分性较差的问题,文中提出一种基于核迁移稀疏编码的跨域图像分类方法,并应用于图像分类.首先将图像特征和字典映射到一个高维的再生核希尔伯特空间,使得线性不可分问题变为线性可分问题.然后在高维特征空间中对每个样本数据进行表示.文中算法不仅有效地处理非线性结构数据,而且考虑了源域和目标域的分布差异以及几何结构信息,获得更为鲁棒的稀疏表达,提高跨域图像分类精度.

To deal with the problem that the data samples with non-linear distribution are poorly separable in the original feature space, in this paper, a kernel transfer sparse soding algorithms is proposed for cross-domain image classification. Firstly, the image features and the dictionary are mapped to a high-dimensional reproducing-kernel hilbert space, which makes the problem of linear inseparable problem become a linear separable problem. Then, the samples are individually represented in high dimensional feature space. The proposed algorithm not only effectively handles the the nonlinear structure data, but also considers the distribution differences and geometric structure information between source and target domains, which gain more robust sparse representation and improve cross- domain image classification accuracy.