Locality constrained Graph Optimization for Dimensionality Reduction
Locality constrained Graph Optimization for Dimensionality Reduction
复制标题
用于降维的局部约束图优化
DOI：
10.1016/j.neucom.2017.03.046
复制
发表时间：
2017
期刊：
影响因子：
6
通讯作者：
Yugen Yi
中科院分区：
文献类型：
--
作者：
Jianzhong Wang;Rui Zhao;Yang Wang;Caixia Zheng;Jun Kong;Yugen Yi
近年来，基于图的降维方法由于在图像分类、数据聚类等实际问题中的广泛应用而受到广泛关注。然而，一个不合适的图，不能准确地反映底层结构和分布的输入数据将显着恶化这些方法的性能。在本文中，我们提出了一种新的算法，称为局部约束图优化降维（LC-GODR），以解决现有的基于图的降维方法的局限性。首先，与大多数基于图的降维方法不同，在大多数基于图的降维方法中，图是预先构造的，并且在降维过程中保持不变，我们的LC-GODR将图优化和投影矩阵学习结合到一个联合框架中。因此，在降维过程中，该算法中的图可以自适应地更新。其次，通过在LC-GODR中引入局部性约束，可以发现并保留高维输入数据的局部信息，从而使该算法区别于其他基于图优化的降维方法。此外，还提供了一个有效的更新计划，以解决建议的LC-GODR。在两个UCI和五个图像数据库上进行了广泛的实验，以证明我们的算法的有效性。实验结果表明，所提出的LC-GODR优于其他相关方法。
Recently, graph-based dimensionality reduction methods have attracted much attention due to their widely applications in many practical tasks such as image classification and data clustering. However, an inappropriate graph which cannot accurately reflect the underlying structure and distribution of input data will dramatically deteriorate the performances of these methods. In this paper, we propose a novel algorithm termed Locality Constrained Graph Optimization Dimensionality Reduction (LC-GODR) to address the limitations of existing graph-based dimensionality reduction methods. Firstly, unlike most graph-based dimensionality reduction methods in which the graphs are constructed in advance and kept unchanged during dimensionality reduction, our LC-GODR combines the graph optimization and projection matrix learning into a joint framework. Therefore, the graph in the proposed algorithm can be adaptively updated during the procedure of dimensionality reduction. Secondly, through introducing the locality constraints into our LC-GODR, the local information of high-dimensional input data can be discovered and well preserved, which makes the proposed algorithm distinct from other graph optimization based dimensionality reduction methods. Moreover, an effective updating scheme is also provided to solve the proposed LC-GODR. Extensive experiments on two UCI and five image databases are conducted to demonstrate the effectiveness of our algorithm. The experimental results indicate that the proposed LC-GODR outperforms other related methods.