Xiang Fengtao, Wang Zhengzhi, Yuan Xingsheng
College of Mechatronics Engineering and Automation, National University of Defense Technology, Changsha, Hunan, China.
Appl Opt. 2013 Jul 10;52(20):5022-9. doi: 10.1364/AO.52.005022.
This paper investigates the use of feature dimensionality reduction approaches for high-dimensional data analysis. Most of the existing preserving projection methods are based on similarity, such as the well-known locality-preserving projections, neighborhood-preserving embedding, and sparsity-preserving projections. Here, we propose a simple yet very efficient preserving projection method based on sparsity and dissimilarity for feature extraction, named dissimilarity sparsity-preserving projections, which is an extended version of sparsity-preserving projections. Both projection coefficients and reconstructive residuals are considered in our proposed framework. We give an idea of a "dissimilarity metric" as the measurement of the relationship among the object data. If the value of the dissimilarity metric of two samples is large, the possibility of them belonging to the same class is small. The proposed methods do not have to preset the number of neighbors and heat kernel width, which is one of the important differences from other projection methods. In practical applications, an approximately direct and complete solution is obtained for the proposed algorithm. Experimental results on three widely used face datasets demonstrate that the proposed framework could achieve competitive performance in terms of accuracy and efficiency.
本文研究了特征降维方法在高维数据分析中的应用。现有的大多数保投影方法都是基于相似性的，比如著名的局部保留投影、邻域保留嵌入和稀疏保留投影。在此，我们提出一种基于稀疏性和不相似性的简单而高效的保投影方法用于特征提取，称为不相似性稀疏保留投影，它是稀疏保留投影的扩展版本。在我们提出的框架中同时考虑了投影系数和重构残差。我们提出了一种“不相似性度量”的概念作为对象数据之间关系的度量。如果两个样本的不相似性度量值很大，那么它们属于同一类别的可能性就很小。所提出的方法不必预先设定邻居数量和热核宽度，这是与其他投影方法的一个重要区别。在实际应用中，所提出的算法能得到一个近似直接且完整的解。在三个广泛使用的人脸数据集上的实验结果表明，所提出的框架在准确性和效率方面能够取得有竞争力的性能。
