Recently semi-supervised non-negative matrix factorization (NMF) has received a lot of attentions in computer vision, information retrieval and pattern recognition, because that partial label information can produce considerable improvement in learning accuracy of the algorithms. However, the existing semi-supervised NMF algorithms cannot make full use of label information, that is, they cannot guarantee that the labeled data of different clusters will not be classified into a same group in the new representation space. In this paper, we propose a novel discriminative semi-supervised NMF (DSSNMF) algorithm, which utilizes the label information of a fraction of the data as a discriminative constraint. We explore the proposed DSSNMF method with two different cost function formulations and provide the corresponding update rules for the optimization problems. Empirical experiments demonstrate the effectiveness of our novel algorithm through a set of evaluations based on real-world applications.

最近，半监督非负矩阵分解（NMF）在计算机视觉，信息检索和模式识别方面引起了广泛关注，因为部分标签信息可以极大地提高算法的学习准确性。但是，现有的半监督NMF算法不能充分利用标签信息，也就是说，它们不能保证在新的表示空间中，不同聚类的标签数据不会被归类为同一组。在本文中，我们提出了一种新颖的区分半监督NMF（DSSNMF）算法，该算法利用数据的一小部分的标签信息作为区分约束。我们用两种不同的成本函数公式探索提出的DSSNMF方法，并为优化问题提供相应的更新规则。经验实验通过一组基于实际应用的评估，证明了我们新颖算法的有效性。