Low-Rank Kernel-Based Semisupervised Discriminant Analysis

Zu, Baokai; Xia, Kewen; Shui-dong, Dai; Aslam, Nelofar

doi:10.1155/2016/2783568

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…The work that is most closely related to ours is the low-rank kernel-based Semisupervised Discriminant Analysis [ 25 ], which is my previous research. The LRR is used as the kernel in the KSDA [ 2 ].…”

Section: Related Workmentioning

confidence: 99%

A Novel Graph Constructor for Semisupervised Discriminant Analysis: Combined Low-Rank and k-Nearest Neighbor Graph

Xia

Pan

et al. 2017

Computational Intelligence and Neuroscience

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Semisupervised Discriminant Analysis (SDA) is a semisupervised dimensionality reduction algorithm, which can easily resolve the out-of-sample problem. Relative works usually focus on the geometric relationships of data points, which are not obvious, to enhance the performance of SDA. Different from these relative works, the regularized graph construction is researched here, which is important in the graph-based semisupervised learning methods. In this paper, we propose a novel graph for Semisupervised Discriminant Analysis, which is called combined low-rank and k-nearest neighbor (LRKNN) graph. In our LRKNN graph, we map the data to the LR feature space and then the kNN is adopted to satisfy the algorithmic requirements of SDA. Since the low-rank representation can capture the global structure and the k-nearest neighbor algorithm can maximally preserve the local geometrical structure of the data, the LRKNN graph can significantly improve the performance of SDA. Extensive experiments on several real-world databases show that the proposed LRKNN graph is an efficient graph constructor, which can largely outperform other commonly used baselines.

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Section: Related Workmentioning

confidence: 99%