Feature subspace learning is a crucial issue in pattern analysis. However, it remains challenging when partial samples are unlabeled, which will cause weak discrimination. In this paper, we present a novel semi-supervised learning model that is capable of utilizing labeled and unlabeled training data simultaneously to learn discriminative feature subspace while preserving their locality. To achieve this goal, we joint learning feature subspace and completed labels. In the framework, low rank representation model is firstly exploited to explore the similarity relationship among all training samples, including labeled and unlabeled data. Then, the learned representation coefficients are used to generate a dynamic neighbor graph for designing the locality preservation constraints on both of label propagation and feature subspace. Finally, the prediction and true label are used to enforce the discrimination of feature subspace in the semantic space. Extensive experiment indicates that our proposed approach is more competitive than other comparison methods, while the model shows more robustness when training datasets are contaminated with noise.

特征子空间学习是模式分析中的关键问题。然而，当未标记部分样品时，仍然存在挑战，这将导致分辨力较弱。在本文中，我们提出了一种新颖的半监督学习模型，该模型能够同时利用标记的和未标记的训练数据来学习判别性特征子空间，同时保留其局部性。为了实现这个目标，我们联合学习特征子空间和完整的标签。在该框架中，首先利用低秩表示模型来探索所有训练样本之间的相似关系，包括标记数据和未标记数据。然后，将学习到的表示系数用于生成动态邻居图，以设计对标签传播和特征子空间的局部性保存约束。最后，预测和真实标签用于增强语义空间中特征子空间的区分。大量实验表明，我们提出的方法比其他比较方法更具竞争力，而当训练数据集被噪声污染时，该模型显示出更高的鲁棒性。