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Semi-Supervised Classification Based on Low Rank Representation

College of Computer and Information Science, Southwest University, Chongqing 400715, China
Author to whom correspondence should be addressed.
Academic Editor: Javier Del Ser Lorente
Algorithms 2016, 9(3), 48;
Received: 1 June 2016 / Revised: 14 July 2016 / Accepted: 20 July 2016 / Published: 22 July 2016
PDF [841 KB, uploaded 22 July 2016]


Graph-based semi-supervised classification uses a graph to capture the relationship between samples and exploits label propagation techniques on the graph to predict the labels of unlabeled samples. However, it is difficult to construct a graph that faithfully describes the relationship between high-dimensional samples. Recently, low-rank representation has been introduced to construct a graph, which can preserve the global structure of high-dimensional samples and help to train accurate transductive classifiers. In this paper, we take advantage of low-rank representation for graph construction and propose an inductive semi-supervised classifier called Semi-Supervised Classification based on Low-Rank Representation (SSC-LRR). SSC-LRR first utilizes a linearized alternating direction method with adaptive penalty to compute the coefficient matrix of low-rank representation of samples. Then, the coefficient matrix is adopted to define a graph. Finally, SSC-LRR incorporates this graph into a graph-based semi-supervised linear classifier to classify unlabeled samples. Experiments are conducted on four widely used facial datasets to validate the effectiveness of the proposed SSC-LRR and the results demonstrate that SSC-LRR achieves higher accuracy than other related methods. View Full-Text
Keywords: semi-supervised classification; graph construction; low-rank representation semi-supervised classification; graph construction; low-rank representation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Hou, X.; Yao, G.; Wang, J. Semi-Supervised Classification Based on Low Rank Representation. Algorithms 2016, 9, 48.

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