1403/01/10
فردین اخلاقیان طاب

فردین اخلاقیان طاب

مرتبه علمی: دانشیار
ارکید:
تحصیلات: دکترای تخصصی
اسکاپوس: 9635715500
دانشکده: دانشکده مهندسی
نشانی:
تلفن:

مشخصات پژوهش

عنوان
Self-Paced Multi-Label Learning with Diversity
نوع پژوهش
Presentation
کلیدواژه‌ها
Multi-label learning, semi supervised learning, non negative matrix factorization
سال
2019
پژوهشگران Seyed Siamak Ghodsi ، Seyed Amjad Seyedi ، Fardin Akhlaghian Tab ، Mahdi Jalili ، Parham Moradi

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.