Guo Tan, Tan Xiaoheng, Zhang Lei, Xie Chaochen, Deng Lu
College of Communication Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel). 2017 Jun 22;17(7):1475. doi: 10.3390/s17071475.
Recently, low-rank and sparse model-based dimensionality reduction (DR) methods have aroused lots of interest. In this paper, we propose an effective supervised DR technique named block-diagonal constrained low-rank and sparse-based embedding (BLSE). BLSE has two steps, i.e., block-diagonal constrained low-rank and sparse representation (BLSR) and block-diagonal constrained low-rank and sparse graph embedding (BLSGE). Firstly, the BLSR model is developed to reveal the intrinsic intra-class and inter-class adjacent relationships as well as the local neighborhood relations and global structure of data. Particularly, there are mainly three items considered in BLSR. First, a sparse constraint is required to discover the local data structure. Second, a low-rank criterion is incorporated to capture the global structure in data. Third, a block-diagonal regularization is imposed on the representation to promote discrimination between different classes. Based on BLSR, informative and discriminative intra-class and inter-class graphs are constructed. With the graphs, BLSGE seeks a low-dimensional embedding subspace by simultaneously minimizing the intra-class scatter and maximizing the inter-class scatter. Experiments on public benchmark face and object image datasets demonstrate the effectiveness of the proposed approach.
最近，基于低秩和稀疏模型的降维（DR）方法引起了广泛关注。在本文中，我们提出了一种有效的监督降维技术，称为块对角约束低秩稀疏嵌入（BLSE）。BLSE有两个步骤，即块对角约束低秩稀疏表示（BLSR）和块对角约束低秩稀疏图嵌入（BLSGE）。首先，开发BLSR模型以揭示内在的类内和类间相邻关系以及数据的局部邻域关系和全局结构。特别地，BLSR主要考虑三个项。第一，需要一个稀疏约束来发现局部数据结构。第二，纳入一个低秩准则来捕获数据中的全局结构。第三，对表示施加块对角正则化以促进不同类之间的区分。基于BLSR，构建信息丰富且有区分力的类内和类间图。利用这些图，BLSGE通过同时最小化类内散度和最大化类间散度来寻找低维嵌入子空间。在公共基准人脸和物体图像数据集上的实验证明了所提方法的有效性。
