Please use this identifier to cite or link to this item:
|Title:||Unified solution to nonnegative data factorization problems|
|Authors:||Liu, X. |
|Citation:||Liu, X., Yan, S., Yan, J., Jin, H. (2009). Unified solution to nonnegative data factorization problems. Proceedings - IEEE International Conference on Data Mining, ICDM : 307-316. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDM.2009.18|
|Abstract:||In this paper, we restudy the non-convex data factorization problems (regularized or not, unsupervised or supervised), where the optimization is confined in the nonnegative orthant, and provide a unified convergency provable solution based on multiplicative nonnegative update rules. This solution is general for optimization problems with block-wisely quadratic objective functions, and thus direct update rules can be derived by skipping over the tedious specific procedure deduction process and algorithmic convergence proof. By taking this unified solution as a general template, we i) re-explain several existing nonnegative data factorization algorithms, ii) develop a variant of nonnegative matrix factorization formulation for handling out-of-sample data, and iii) propose a new nonnegative data factorization algorithm, called Correlated Co-Decomposition (CCD), to simultaneously factorize two feature spaces by exploring the inter-correlated information. Experiments on both face recognition and multi-label image annotation tasks demonstrate the wide applicability of the unified solution as well as the effectiveness of two proposed new algorithms. © 2009 IEEE.|
|Source Title:||Proceedings - IEEE International Conference on Data Mining, ICDM|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 11, 2019
WEB OF SCIENCETM
checked on Jan 1, 2019
checked on Dec 29, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.