Bidirectional Nonnegative Deep Model and Its Optimization in Learning. (16th November 2016)

• Record Type: Journal Article
• Authors: Zeng, Xianhua; He, Zhengyi; Yu, Hong; Qu, Shengwei
• Other Names: Liu Tongliang Academic Editor.
• Is Part Of: Journal of optimization. Volume 2016(2016)
• Publication Date: 2016-11-16
• Subjects: Mathematical optimization -- Periodicals; Structural optimization -- Periodicals; Mathematical optimization; Structural optimization; Periodicals; Electronic journals; 519.6
• DOI: 10.1155/2016/5975120
• Languages: English
• ISSNs: 2356-752X

Abstract

Abstract : Nonnegative matrix factorization (NMF) has been successfully applied in signal processing as a simple two-layer nonnegative neural network. Projective NMF (PNMF) with fewer parameters was proposed, which projects a high-dimensional nonnegative data onto a lower-dimensional nonnegative subspace. Although PNMF overcomes the problem of out-of-sample of NMF, it does not consider the nonlinear characteristic of data and is only a kind of narrow signal decomposition method. In this paper, we combine the PNMF with deep learning and nonlinear fitting to propose a bidirectional nonnegative deep learning (BNDL) model and its optimization learning algorithm, which can obtain nonlinear multilayer deep nonnegative feature representation. Experiments show that the proposed model can not only solve the problem of out-of-sample of NMF but also learn hierarchical nonnegative feature representations with better clustering performance than classical NMF, PNMF, and Deep Semi-NMF algorithms.