Details

Autor(en) / Beteiligte

• Xiao, Yan-Hui
• Zhu, Zhen-Feng
• Zhao, Yao
• Wei, Yun-Chao

Titel

Class-Driven Non-Negative Matrix Factorization for Image Representation

Ist Teil von

• Journal of computer science and technology, 2013-09, Vol.28 (5), p.751-761

Ort / Verlag

Boston: Springer US

Erscheinungsjahr

2013

Quelle

SpringerLink

Beschreibungen/Notizen

• Non-negative matrix fa~torization （NMF） is a useful technique to learn a parts-based representation by decom- posing the original data matrix into a basis set and coefficients with non-negative constraints. However, as an unsupervised method, the original NMF cannot utilize the discriminative class information. In this paper, we propose a semi-supervised class-driven NMF method to associate a class label with each basis vector by introducing an inhomogeneous representation cost constraint. This constraint forces the learned basis vectors to represent better for their own classes but worse for the others. Therefore, data samples in the same class will have similar representations, and consequently the discriminability in new representations could be boosted. Some experiments carried out on several standard databases validate the effectiveness of our method in comparison with the state-of-the-art approaches.