Journal
NEUROCOMPUTING
Volume 74, Issue 18, Pages 3800-3807Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2011.07.020
Keywords
Concept factorization; Dimensionality reduction; Semi-supervised learning
Categories
Funding
- National Natural Science Foundation of China [60875044]
- National Key Basic Research Foundation of China [2009CB320801]
Ask authors/readers for more resources
Non-negative matrix factorization (NMF) has become a popular technique for finding low-dimensional representations of data. While the standard NMF can only be performed in the original feature space, one variant of NMF, named concept factorization, can be naturally kernelized and inherits all the strengths of NMF. To make use of label information, we propose a semi-supervised concept factorization technique called discriminative concept factorization (DCF) for data representation in this paper. DCF adopts a unified objective to combine the task of data reconstruction with the task of classification. These two tasks have mutual impacts on each other, which results in a concept factorization adapted to the classification task and a classifier built on the low-dimensional representations. Furthermore, we develop an iterative algorithm to solve the optimization problem through alternative convex programming. Experimental results on three real-word classification tasks demonstrate the effectiveness of DCF. (C) 2011 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available