مشخصات پژوهش

عنوان	Self-Paced Multi-Label Learning with Diversity
نوع پژوهش	مقاله ارائه شده کنفرانسی
کلیدواژه‌ها	Multi-label learning, semi supervised learning, non negative matrix factorization
چکیده	The major challenge of learning from multi-label data has arisen from the overwhelming size of label space which makes this problem NP-hard. This problem can be alleviated by gradually involving easy to hard tags into the learning process. Besides, the utilization of a diversity maintenance approach avoids overfitting on a subset of easy labels. In this paper, we propose a self-paced multi-label learning with diversity (SPMLD) which aims to cover diverse labels with respect to its learning pace. In addition, the proposed framework is applied to an efficient correlation-based multi-label method. The non-convex objective function is optimized by an extension of the block coordinate descent algorithm. Empirical evaluations on real-world datasets with different dimensions of features and labels imply the effectiveness of the proposed predictive model.
پژوهشگران	پرهام مرادی دولت آبادی (نفر پنجم)، مهدی جلیلی (نفر چهارم)، فردین اخلاقیان طاب (نفر سوم)، سید امجد سیدی (نفر دوم)، سید سیامک قدسی (نفر اول)