دانلود مقاله ISI انگلیسی شماره 107040
ترجمه فارسی عنوان مقاله

تعبیه سازگاری چند منبع با انتخاب ویژگی با استفاده از اطلاعات همبستگی

عنوان انگلیسی
Multi-source adaptation embedding with feature selection by exploiting correlation information
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
107040 2018 17 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Knowledge-Based Systems, Volume 143, 1 March 2018, Pages 208-224

پیش نمایش مقاله
پیش نمایش مقاله  تعبیه سازگاری چند منبع با انتخاب ویژگی با استفاده از اطلاعات همبستگی

چکیده انگلیسی

While feature selection has recently received much research attention, less or limited effort has been made on improving the performance of feature selection by leveraging the shared knowledge from other related domains. Besides, multi-source adaptation embedding by exploiting the correlation information among domain features and distributions has long been largely unaddressed. To this end, we propose in this paper a robust Multi-source Adaptation Embedding framework with Feature Selection (MAEFS) by exploiting the correlation information via joint l2, 1-norm and trace-norm regularization, and apply it to cross-domain visual recognition. Specifically, to uncover cross-domain invariant subspaces by minimizing the distribution discrepancy between source and target domains, instead of evaluating the importance of each feature individually, MAEFS selects features in a collaborated mode for considering the correlation information among features. Furthermore, multiple feature selection functions for different source adaptation objects are simultaneously learned in a joint framework, which enables MAEFS to utilize the correlated knowledge among multiple source domains via trace-norm regularization, thus facilitating domain invariant embedding. Besides, by employing graph embedding and sparse regression scheme via l2, 1-norm minimization, MAEFS can preserve the original geometrical structure information as well as be robust to some noises or outliers existed in domains. Finally, an efficient iterative algorithm is proposed to optimize MAEFS, whose convergence is theoretically guaranteed. Comprehensive experimental evidence on a large number of visual datasets verifies the effectiveness of the proposed framework.