Constrained Nonnegative Matrix Factorization for Image Representation
Constrained Nonnegative Matrix Factorization for Image Representation
复制标题
图像表示的约束非负矩阵分解
DOI：
10.1109/tpami.2011.217
复制
发表时间：
2012-07-01
影响因子：
23.6
通讯作者：
Huang, Thomas S.
中科院分区：
文献类型：
--
作者：
Liu, Haifeng;Wu, Zhaohui;Huang, Thomas S.
非负矩阵分解（NMF）是一种用于查找非负数据的基于部分的线性表示的流行技术。它已成功地应用于广泛的应用，如模式识别，信息检索和计算机视觉。然而，NMF本质上是一种无监督的方法，不能利用标签信息。在本文中，我们提出了一种新的半监督矩阵分解方法，称为约束非负矩阵分解（CNMF），它结合了标签信息作为额外的约束。具体来说，我们展示了如何明确地结合标签信息，提高了所得到的矩阵分解的鉴别能力。我们探讨了建议CNMF方法与两个成本函数配方，并提供相应的更新解决方案的优化问题。实证实验表明，我们的新算法的有效性相比，国家的最先进的方法，通过一组基于现实世界的应用程序的评估。
Nonnegative matrix factorization (NMF) is a popular technique for finding parts-based, linear representations of nonnegative data. It has been successfully applied in a wide range of applications such as pattern recognition, information retrieval, and computer vision. However, NMF is essentially an unsupervised method and cannot make use of label information. In this paper, we propose a novel semi-supervised matrix decomposition method, called Constrained Nonnegative Matrix Factorization (CNMF), which incorporates the label information as additional constraints. Specifically, we show how explicitly combining label information improves the discriminating power of the resulting matrix decomposition. We explore the proposed CNMF method with two cost function formulations and provide the corresponding update solutions for the optimization problems. Empirical experiments demonstrate the effectiveness of our novel algorithm in comparison to the state-of-the-art approaches through a set of evaluations based on real-world applications.