Abstract
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.
Highlights
A Novel Graph Constructor for Semisupervised Discriminant AnalysisReceived 18 June 2016; Revised 21 November 2016; Accepted January 2017; Published February 2017
For the real-world data mining and pattern recognition applications, the labeled data are very expensive or difficult to obtain, while the unlabeled data are often copious and available
We focus on constructing a novel graph for Semisupervised Discriminant Analysis (SDA), capturing the data using low-rank representation (LRR) and utilizing the KNN algorithm to satisfy the algorithmic requirements of SDA
Summary
Received 18 June 2016; Revised 21 November 2016; Accepted January 2017; Published February 2017. Semisupervised Discriminant Analysis (SDA) is a semisupervised dimensionality reduction algorithm, which can 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. We propose a novel graph for Semisupervised Discriminant Analysis, which is called combined low-rank and k-nearest neighbor (LRKNN) graph. 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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