报告地点：行健楼-526
邀请人：孙海琳教授
摘要：Tensor completion refers to the task of estimating the missing data from an incomplete measurement or observation, which is a core problem frequently arising from the areas of big data analysis, computer vision, and network engineering. Due to the multidimensional nature of high-order tensors, the matrix approaches, e.g., matrix factorization and direct matricization of tensors, are often not ideal for tensor completion and recovery. Exploiting the potential periodicity and inherent correlation properties appeared in real-world tensor data, in this talk, we shall incorporate the low-rank and sparse regularization technique to enhance Tucker decomposition for tensor completion. A series of computational experiments on real-world datasets, including color images and face recognition, show that our approach performs better than many existing state-of-the-art matricization and tensorization approaches in terms of achieving higher recovery accuracy. (Joint work with C. Pan, C. Ling, L.Q. Qi, and Y. Xu)
报告人简介：何洪津，副教授，硕士生导师，2012年6月博士毕业于女王调教
计算数学专业，师从孙文瑜教授和韩德仁教授；2013年8月至2014年8月，在台湾中山大学从事博士后研究，师从徐洪坤教授；2012年6月进入杭州电子科技大学理*女王调教-女王调教视频-女王 调教小说工作。主要研究兴趣为数值优化及其在图像处理、机器学习等领域中的应用。