Image and Graphics, International Conference on (2011)
Hefei, Anhui China
Aug. 12, 2011 to Aug. 15, 2011
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICIG.2011.82
Semi-supervised dimensionality reduction is very important in mining high-dimensional data due to the lack of costly labeled data. This paper studies the Semi-supervised Discriminant Analysis (SDA) algorithm, which aims at dimensionality reduction utilizing both limited labeled data and abundant unlabeled data. Different from other relative work, we pay our attention to graph construction, which plays a key role in graph based SSL methods. Inspired by the advances of compressive sensing, we propose a novel graph construction method via group sparsity, which means to constrain the reconstruct data to be sparse for each sample, and constrain the representation in each class to be quite similar. Experimental results show that our method can significantly improve the performance of SDA, and outperform state-of-the-art methods.
semi-supervised learning, graph construction, sparsest representation
H. Gao, N. Yu and L. Zhuang, "A New Graph Constructor for Semi-supervised Discriminant Analysis via Group Sparsity," Image and Graphics, International Conference on(ICIG), Hefei, Anhui China, 2011, pp. 691-695.