Supervised sparse neighbourhood preserving embedding. Issue 3 (4th January 2017)

• Record Type: Journal Article
• Authors: Qian, Liqiang; Zhang, Li; Bao, Xing; Li, Fanzhang; Yang, Jiwen
• Is Part Of: IET image processing. Volume 11:Issue 3(2017)
• Page Start: 190
• Page End: 199
• Publication Date: 2017-01-04
• Subjects: learning (artificial intelligence) -- pattern classification; SSNPE -- unsupervised learning method -- SPP -- NPE -- sparsity preserving projection -- supervised sparse neighbourhood preserving embedding; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0254
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Both neighbourhood preserving embedding (NPE) and sparsity preserving projection (SPP) are unsupervised learning methods, where NPE can preserve the local neighbourhood information of a given dataset and SPP can preserve the sparsely reconstructive relationship of the dataset. However, it is not satisfactory when applying the two methods to classification tasks. First, a modified SPP is presented here. Then this study proposes a supervised sparse neighbourhood preserving embedded algorithm (SSNPE) based on NPE, the modified SPP and the label information of a given task. SSNPE inherits the merits of NPE and SPP, which can preserve not only the local neighbourhood information but also the sparsely reconstructive relationship. The connection between SSNPE and both NPE and SPP is discussed. Experimental results on the datasets of UCI, ORL and MNIST indicate that the proposed method is effective.