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High-dimensional and sparse (HiDS) matrices are commonly encountered in many big data-related industrial applications like recommender systems. When acquiring useful patterns from them, non-negative matrix factorization (NMF) models have proven to be highly effective because of their fine representativeness of non-negative data. However, current NMF techniques suffer from a) inefficiency in addressing...
With the exploration of the World Wide Web, more and more entities are involved in various online applications, e.g., recommender systems and social network services. In such context, high-dimensional sparse matrices describing the relationships among them are frequently encountered. It is highly important to develop efficient non-negative latent factor (NLF) models for these high-dimensional sparse...
This paper describes the recent educational activities and programs organized by the IEEE Engineering in Medicine and Biology Society (EMBS) Xiamen University Student Club. The educational programs covered the undergraduate student mentoring program, seminar series, the top-quality course project, and student scientific projects and contests. These activities have successfully cultivated our students...
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