具有特征选择的多源自适应分类框架

对于现有的多源自适应学习方案无法有效区分多个源域中的有用信息并迁移至目标域的问题，提出一种具有特征选择的多源自适应分类框架（MACFFS），并将特征选择和共享特征子空间学习整合到统一框架中进行联合特征学习。具体来说，MACFFS将来自多个源域的特征数据投影至不同的潜在空间中来学习得到多个源域分类模型，实现目标域的分类。然后，将得到的多个分类结果进行整合用于目标域分类模型的学习。此外，框架还利用L_2，1范数稀疏回归代替传统的基于L₂范数的最小二乘回归来提高鲁棒性。最后，把多种现有方法在两项任务中与MACFFS进行实验比较分析。实验结果表明，与现有方法中表现最好的DSM相比，MACFFS节省了接近1/4的计算时间，并且提升了大约2%的识别率。总的来说，MACFFS结合了机器学习、统计学习等相关知识，为多源自适应方法提供了一个新的思路，且该方法在现实场景下的识别应用中比现有方法具有更好的性能。

Multi-source adaptation classification framework with feature selection

For the problem that the existing multi-source adaptation learning schemes cannot effectively distinguish the useful information in multi-source domains and transfer the information to the target domain, a Multi-source Adaptation Classification Framework with Feature Selection (MACFFS) was proposed. Feature selection and shared feature subspace learning were integrated into a unified framework by MACFFS for joint feature learning. Specifically, multiple source domain classification models were learned and obtained by MACFFS through mapping feature data from multiple source domains into different latent spaces, so as to realize the classification of target domains. Then, the obtained multiple classification results were integrated for the learning of the target domain classification model. In addition, L_2,1 norm sparse regression was used to replace the traditional least squares regression based on L₂ norm by the framework to improve the robustness. Finally, a variety of existing methods were used to perform experimental comparison and analysis with MACFFS in two tasks. Experimental results show that, compared with the best performing Domain Selection Machine (DSM) in the existing methods, MACFFS has nearly 1/4 of the calculation time saved, and the recognition rate of about 2% improved. In general, with machine learning, statistical learning and other related knowledge combined, MACFFS provides a new idea for the multi-source adaptation method. Furthermore, this method has better performance than the existing methods in recognition applications in real scenes, which had been experimentally proven.