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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 <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-nearest neighbor (LRKNN) graph. In our LRKNN graph, we map the data to the LR feature space and then the <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mi>k<\/mml:mi><mml:mi mathvariant=\"normal\">N<\/mml:mi><mml:mi mathvariant=\"normal\">N<\/mml:mi><\/mml:math> is adopted to satisfy the algorithmic requirements of SDA. Since the low-rank representation can capture the global structure and the <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-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.<\/jats:p>","DOI":"10.1155\/2017\/9290230","type":"journal-article","created":{"date-parts":[[2017,2,20]],"date-time":"2017-02-20T16:04:19Z","timestamp":1487606659000},"page":"1-11","source":"Crossref","is-referenced-by-count":5,"title":["A Novel Graph Constructor for Semisupervised Discriminant Analysis: Combined Low-Rank and <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-Nearest Neighbor Graph"],"prefix":"10.1155","volume":"2017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7070-831X","authenticated-orcid":true,"given":"Baokai","family":"Zu","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China"},{"name":"Key Lab of Big Data 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