Title :
Compressed Nonnegative Sparse Coding
Author :
Wang, Fei ; Li, Ping
Author_Institution :
Dept. of Stat. Sci., Cornell Univ., Ithaca, NY, USA
Abstract :
Sparse Coding (SC), which models the data vectors as sparse linear combinations over basis vectors, has been widely applied in machine learning, signal processing and neuroscience. In this paper, we propose a dual random projection method to provide an efficient solution to Nonnegative Sparse Coding (NSC) using small memory. Experiments on real world data demonstrate the effectiveness of the proposed method.
Keywords :
data reduction; matrix decomposition; source coding; vectors; Nonnegative Sparse Coding; data vector; dual random projection method; sparse linear combination;
Conference_Titel :
Data Mining (ICDM), 2010 IEEE 10th International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4244-9131-5
Electronic_ISBN :
1550-4786
DOI :
10.1109/ICDM.2010.162
