KMS Chongqing Institute of Green and Intelligent Technology, CAS
| Regularizaed extraction of non-negative latent factors from high-dimensional sparse matrices | |
Luo, Xin1 ; Li, Shuai2; Zhou, Mengchu3
| |
| 2016 | |
| 摘要 | 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 relationships because of a) their ability to extract useful knowledge from them; b) their fulfillment of the non-negativity constraints for representing most non-negative industrial data; and c) their high computational and storage efficiency on high-dimensional sparse matrices. However, due to the imbalanced distribution of known data in such a matrix, it is necessary to investigate the regularization effect in NLF models. We first review the NLF model briefly. Then we propose to integrate the frequency-weight on each involved entity into its Tikhonov regularization terms, for representing the imbalanced data from a high-dimensional sparse matrix. Experimental results on industrial-size matrices indicate that the proposed scheme is effective in improving the performance of the NLF model in missing-data-estimation. © 2016 IEEE. |
| 语种 | 英语 |
| DOI | 10.1109/SMC.2016.7844408 |
| 会议(录)名称 | 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
 |
| 页码 | 1221-1226 |
| 收录类别 | EI |
| 会议地点 | Budapest, Hungary |
| 会议日期 | October 9, 2016 - October 12, 2016 |
