Sparse low-rank matrix approximation for data compression

• Main Authors: Hou, Junhui, Chau, Lap-Pui, Magnenat-Thalmann, Nadia, He, Ying
• 其他作者: School of Computer Science and Engineering
• 格式: Article
• 語言: English
• 出版: 2018
• 主題: Optimization; DRNTU::Engineering::Computer science and engineering; Data Compression; DRNTU::Engineering::Electrical and electronic engineering
• 在線閱讀: https://hdl.handle.net/10356/89401; http://hdl.handle.net/10220/46229

Low-rank matrix approximation (LRMA) is a powerful technique for signal processing and pattern analysis. However, its potential for data compression has not yet been fully investigated. In this paper, we propose sparse LRMA (SLRMA), an effective computational tool for data compression. SLRMA extends...